1- Problem:¶
Airline customer satisfaction is a critical factor for airlines' success. in today's competitive market, airlines must constantly strive to improve their customer satisfaction to attract and retain passengers.The dataset provided pertains to Invistico Airlines, an airline organization. It contains customer information, including feedback and flight data from previous passengers. The primary objective of this dataset is to predict the likelihood of satisfaction for potential future customers based on a range of parameter values. Furthermore, the airline aims to identify specific aspects of their services that require greater emphasis to ensure higher levels of customer satisfaction.
2- Data mining task:¶
Data mining within the scope of our dataset involves the utilization of advanced analytical techniques to uncover valuable insights and patterns relevant to airline customer satisfaction. The primary goal is to predict the class label that signifies whether a passenger is satisfied or dissatisfied with their airline experience through classification and clustering techniques. By applying data mining algorithms . The predictive aspect of data mining entails constructing predictive models that can categorize passengers into specific classes, such as "Satisfied" or "Not Satisfied," based on their characteristics and the identified influential factors. These models enable airlines to take proactive measures to enhance passenger satisfaction.
3- Data:¶
This extensive dataset, comprising 129,880 rows and 23 columns, is sourced from diverse channels such as surveys, customer reviews, and social media data. It will undergo thorough data mining procedures aimed at uncovering the pivotal factors that influence customer satisfaction. These influential determinants span a broad spectrum, encompassing variables such as service quality, ticket pricing, flight reliability, and more
Data dictionary:
| Attribute | Description | Type | Possible Values |
|---|---|---|---|
| Satisfaction | whether the client is satisfied or not | Binary | Satisfied - Dissatisfied |
| Gender | the gender of the client | Binary | Female - Male |
| Customer Type | whether the client is loyal or not | Binary | Loyal Customer - Disloyal Customer |
| Age | The age of the customer | Numeric | Between 7 - 85 |
| Type of travel | Purpose of the flight | Binary | Personal travel - Business travel |
| Class | Type of airplane seat | Nominal | Eco - Eco plus - Business |
|Flight Distance|how long is the flight distance| Numeric | From 50 to 6951 |Seat comfort|is the seat comfortable or not| Ordinal | From 0 to 5 | |Departure/Arrival time convenient|if the time is convenient | Ordinal | From 0 to 5 | |Food and drink|what is the quality of the food and drink| Ordinal | From 0 to 5 | |Gate location|the client's rate about the gate location| Ordinal | From 0 to 5 | |Inflight WiFi service|the client's rate for this service| Ordinal | From 0 to 5 | |Inflight entertainment|if the flight contain an entertainment services | Ordinal | From 0 to 5 | |Online support|the client's rate for this service| Ordinal | From 0 to 5 | |Ease of Online Booking|the client's rate about the online booking | Ordinal | From 0 to 5 | |On-board service|the client's rate about the On-board service| Ordinal | From 0 to 5 | |Leg room service|does the client have a space for his legs| Ordinal | From 0 to 5 | |Baggage handling|does the flight have this service and how is it| Ordinal | From 0 to 5 | |Check-In service|the client's rate for this service| Ordinal | From 0 to 5 | |Cleanliness|how clean is the plane| Ordinal | From 0 to 5 | |Online boarding|the client's rate for this service| Ordinal | From 0 to 5 | |Departure Delay in minutes|how many minutes does the Departure delayed| Numeric | From 0 to 1592 | |Arrival Delay in minutes|how many minutes does the arrival delayed| Numeric | From 0 to 1584 |
data = read.csv("Invistico_Airline.csv")
Because of the overwhelming volume of objects, our computers are unable to efficiently process such a large dataset. As a result, we have opted to randomly delete a portion of the rows
nrow(data)
# Set a random seed for reproducibility
set.seed(1234)
# Determine the number of rows you want to delete
num_rows_to_delete <- 120000 # Adjust this number as needed
# Generate random row indices to delete
rows_to_delete <- sample(nrow(data), num_rows_to_delete)
# Keep only the rows that are not in the rows_to_delete vector
data <- data[-rows_to_delete, ]
row_count <- nrow(data)
print(row_count)
[1] 9880
library(readr)
library(tidyr)
library(stringr)
library(dplyr)
library(ggplot2)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
install.packages("outliers")
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
library(outliers)
head(data)
| satisfaction | Gender | Customer.Type | Age | Type.of.Travel | Class | Flight.Distance | Seat.comfort | Departure.Arrival.time.convenient | Food.and.drink | ⋯ | Online.support | Ease.of.Online.booking | On.board.service | Leg.room.service | Baggage.handling | Checkin.service | Cleanliness | Online.boarding | Departure.Delay.in.Minutes | Arrival.Delay.in.Minutes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <chr> | <chr> | <chr> | <int> | <chr> | <chr> | <int> | <int> | <int> | <int> | ⋯ | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | |
| 23 | satisfied | Female | Loyal Customer | 25 | Personal Travel | Eco | 2122 | 0 | 1 | 0 | ⋯ | 4 | 2 | 4 | 1 | 3 | 1 | 3 | 2 | 0 | 0 |
| 30 | satisfied | Female | Loyal Customer | 35 | Personal Travel | Eco | 3695 | 0 | 1 | 0 | ⋯ | 4 | 2 | 2 | 3 | 4 | 4 | 3 | 4 | 0 | 0 |
| 34 | satisfied | Female | Loyal Customer | 26 | Personal Travel | Eco | 2408 | 0 | 1 | 0 | ⋯ | 4 | 4 | 1 | 4 | 4 | 2 | 3 | 4 | 0 | 0 |
| 37 | satisfied | Female | Loyal Customer | 10 | Personal Travel | Eco | 3209 | 0 | 1 | 0 | ⋯ | 4 | 4 | 4 | 3 | 3 | 1 | 4 | 4 | 0 | 0 |
| 50 | satisfied | Male | Loyal Customer | 34 | Personal Travel | Eco | 1816 | 0 | 1 | 0 | ⋯ | 4 | 4 | 1 | 1 | 2 | 3 | 2 | 4 | 0 | 0 |
| 64 | satisfied | Female | Loyal Customer | 11 | Personal Travel | Eco | 1761 | 0 | 1 | 0 | ⋯ | 3 | 3 | 1 | 2 | 3 | 2 | 3 | 3 | 0 | 0 |
print(data)
satisfaction Gender Customer.Type Age Type.of.Travel Class
23 satisfied Female Loyal Customer 25 Personal Travel Eco
30 satisfied Female Loyal Customer 35 Personal Travel Eco
34 satisfied Female Loyal Customer 26 Personal Travel Eco
37 satisfied Female Loyal Customer 10 Personal Travel Eco
50 satisfied Male Loyal Customer 34 Personal Travel Eco
64 satisfied Female Loyal Customer 11 Personal Travel Eco
87 satisfied Female Loyal Customer 9 Personal Travel Eco
92 satisfied Female Loyal Customer 33 Personal Travel Eco
94 satisfied Female Loyal Customer 23 Personal Travel Eco Plus
115 satisfied Female Loyal Customer 9 Personal Travel Eco
125 satisfied Female Loyal Customer 32 Personal Travel Eco
134 satisfied Female Loyal Customer 64 Personal Travel Eco
135 satisfied Female Loyal Customer 37 Personal Travel Eco
160 satisfied Female Loyal Customer 25 Personal Travel Eco
167 satisfied Female Loyal Customer 28 Personal Travel Eco
169 satisfied Female Loyal Customer 26 Personal Travel Eco
178 satisfied Female Loyal Customer 39 Personal Travel Eco
185 satisfied Female Loyal Customer 49 Personal Travel Eco
192 satisfied Female Loyal Customer 47 Personal Travel Eco
213 satisfied Male Loyal Customer 22 Personal Travel Eco
234 satisfied Female Loyal Customer 54 Personal Travel Eco
246 satisfied Female Loyal Customer 29 Personal Travel Eco
251 satisfied Female Loyal Customer 10 Personal Travel Business
276 satisfied Male Loyal Customer 65 Personal Travel Eco
278 satisfied Male Loyal Customer 50 Personal Travel Eco
283 satisfied Male Loyal Customer 44 Personal Travel Eco
288 satisfied Male Loyal Customer 49 Personal Travel Business
293 satisfied Female Loyal Customer 36 Personal Travel Eco Plus
307 satisfied Male Loyal Customer 41 Personal Travel Business
313 satisfied Male Loyal Customer 8 Personal Travel Eco
344 satisfied Male Loyal Customer 34 Personal Travel Eco
352 satisfied Male Loyal Customer 67 Personal Travel Eco
389 satisfied Male Loyal Customer 21 Personal Travel Business
404 satisfied Male Loyal Customer 54 Personal Travel Eco
412 satisfied Male Loyal Customer 56 Personal Travel Eco
420 satisfied Female Loyal Customer 34 Personal Travel Eco
428 satisfied Male Loyal Customer 19 Personal Travel Eco
434 satisfied Female Loyal Customer 69 Personal Travel Eco
454 satisfied Female Loyal Customer 48 Personal Travel Business
456 satisfied Female Loyal Customer 36 Personal Travel Eco
478 satisfied Male Loyal Customer 23 Personal Travel Eco
498 satisfied Female Loyal Customer 67 Personal Travel Eco
511 satisfied Male Loyal Customer 37 Personal Travel Eco
514 satisfied Male Loyal Customer 56 Personal Travel Eco
533 satisfied Female Loyal Customer 50 Personal Travel Eco
543 satisfied Female Loyal Customer 55 Personal Travel Eco
545 satisfied Female Loyal Customer 51 Personal Travel Eco
581 satisfied Male Loyal Customer 11 Personal Travel Eco
592 satisfied Male Loyal Customer 51 Personal Travel Eco
599 satisfied Female Loyal Customer 29 Personal Travel Eco
611 satisfied Female Loyal Customer 25 Personal Travel Eco Plus
612 satisfied Male Loyal Customer 52 Personal Travel Eco
620 satisfied Male Loyal Customer 29 Personal Travel Eco
636 satisfied Male Loyal Customer 23 Personal Travel Eco
643 satisfied Female Loyal Customer 38 Personal Travel Eco
652 satisfied Male Loyal Customer 48 Personal Travel Eco
688 satisfied Female Loyal Customer 60 Personal Travel Eco
703 satisfied Male Loyal Customer 25 Personal Travel Eco
709 satisfied Female Loyal Customer 47 Personal Travel Eco Plus
747 satisfied Male Loyal Customer 20 Personal Travel Eco
758 satisfied Female Loyal Customer 53 Personal Travel Eco
762 satisfied Male Loyal Customer 22 Personal Travel Eco
764 satisfied Female Loyal Customer 33 Personal Travel Eco
769 satisfied Male Loyal Customer 61 Personal Travel Eco
773 satisfied Female Loyal Customer 66 Personal Travel Eco
782 satisfied Male Loyal Customer 14 Personal Travel Eco Plus
807 satisfied Female Loyal Customer 33 Personal Travel Eco
835 satisfied Female Loyal Customer 26 Personal Travel Business
854 satisfied Female Loyal Customer 70 Personal Travel Eco
880 satisfied Female Loyal Customer 63 Personal Travel Eco
885 dissatisfied Male Loyal Customer 59 Personal Travel Eco Plus
890 dissatisfied Male Loyal Customer 25 Personal Travel Eco
903 satisfied Female Loyal Customer 67 Personal Travel Eco
910 dissatisfied Male Loyal Customer 56 Personal Travel Business
915 satisfied Female Loyal Customer 52 Personal Travel Business
916 dissatisfied Male Loyal Customer 31 Personal Travel Eco
920 dissatisfied Male Loyal Customer 9 Personal Travel Eco
923 dissatisfied Male Loyal Customer 56 Personal Travel Eco
929 dissatisfied Male Loyal Customer 14 Personal Travel Eco
962 satisfied Female Loyal Customer 65 Personal Travel Eco
995 dissatisfied Male Loyal Customer 40 Personal Travel Eco
1004 satisfied Female Loyal Customer 43 Personal Travel Eco Plus
1044 dissatisfied Male Loyal Customer 46 Personal Travel Eco
1058 satisfied Female Loyal Customer 33 Personal Travel Eco
1061 satisfied Female Loyal Customer 31 Personal Travel Eco
1064 satisfied Female Loyal Customer 18 Personal Travel Eco
1067 satisfied Female Loyal Customer 23 Personal Travel Eco
1072 dissatisfied Female Loyal Customer 24 Personal Travel Eco Plus
1076 dissatisfied Male Loyal Customer 25 Personal Travel Eco
1092 satisfied Female Loyal Customer 29 Personal Travel Eco
1094 satisfied Female Loyal Customer 29 Personal Travel Eco
1096 dissatisfied Male Loyal Customer 53 Personal Travel Eco Plus
1114 satisfied Female Loyal Customer 57 Personal Travel Eco
1146 dissatisfied Male Loyal Customer 64 Personal Travel Eco
1156 dissatisfied Male Loyal Customer 48 Personal Travel Eco
1178 dissatisfied Male Loyal Customer 51 Personal Travel Eco
1179 satisfied Female Loyal Customer 48 Personal Travel Eco Plus
1186 dissatisfied Male Loyal Customer 70 Personal Travel Eco
1188 satisfied Female Loyal Customer 47 Personal Travel Eco
1193 satisfied Female Loyal Customer 44 Personal Travel Eco Plus
1213 satisfied Female Loyal Customer 47 Personal Travel Eco
1214 satisfied Female Loyal Customer 22 Personal Travel Eco
1225 satisfied Female Loyal Customer 17 Personal Travel Eco
1229 satisfied Female Loyal Customer 27 Personal Travel Eco
1240 satisfied Female Loyal Customer 14 Personal Travel Eco
1246 satisfied Female Loyal Customer 54 Personal Travel Eco
1249 dissatisfied Male Loyal Customer 22 Personal Travel Eco
1264 satisfied Female Loyal Customer 64 Personal Travel Eco
1340 satisfied Female Loyal Customer 13 Personal Travel Eco
1351 satisfied Female Loyal Customer 50 Personal Travel Eco
1387 satisfied Female Loyal Customer 57 Personal Travel Eco
1396 satisfied Female Loyal Customer 46 Personal Travel Eco
1397 dissatisfied Male Loyal Customer 28 Personal Travel Eco Plus
1404 satisfied Female Loyal Customer 47 Personal Travel Eco
1443 dissatisfied Male Loyal Customer 12 Personal Travel Eco
1450 satisfied Female Loyal Customer 15 Personal Travel Eco
1455 dissatisfied Male Loyal Customer 61 Personal Travel Eco Plus
1471 satisfied Female Loyal Customer 42 Personal Travel Eco
1475 satisfied Female Loyal Customer 54 Personal Travel Eco Plus
1479 dissatisfied Male Loyal Customer 9 Personal Travel Eco
1489 dissatisfied Male Loyal Customer 28 Personal Travel Eco
1499 satisfied Female Loyal Customer 41 Personal Travel Eco
1506 dissatisfied Male Loyal Customer 65 Personal Travel Eco
1529 satisfied Female Loyal Customer 68 Personal Travel Eco
1559 dissatisfied Male Loyal Customer 26 Personal Travel Eco
1564 satisfied Female Loyal Customer 10 Personal Travel Eco
1576 satisfied Female Loyal Customer 9 Personal Travel Eco
1614 satisfied Female Loyal Customer 56 Personal Travel Eco
1627 satisfied Female Loyal Customer 39 Personal Travel Eco
1636 dissatisfied Male Loyal Customer 15 Personal Travel Eco
1660 satisfied Female Loyal Customer 14 Personal Travel Eco
1661 dissatisfied Male Loyal Customer 43 Personal Travel Eco
1668 satisfied Female Loyal Customer 62 Personal Travel Eco
1685 satisfied Female Loyal Customer 57 Personal Travel Eco
1696 dissatisfied Male Loyal Customer 47 Personal Travel Business
1704 dissatisfied Male Loyal Customer 49 Personal Travel Eco
1722 dissatisfied Male Loyal Customer 10 Personal Travel Eco
1732 dissatisfied Male Loyal Customer 9 Personal Travel Eco
1760 dissatisfied Male Loyal Customer 44 Personal Travel Eco
1761 dissatisfied Male Loyal Customer 61 Personal Travel Eco
1769 dissatisfied Male Loyal Customer 31 Personal Travel Eco
1776 dissatisfied Male Loyal Customer 35 Personal Travel Eco
1802 dissatisfied Male Loyal Customer 34 Personal Travel Eco
1810 satisfied Female Loyal Customer 14 Personal Travel Eco Plus
1812 satisfied Female Loyal Customer 70 Personal Travel Eco
1819 satisfied Female Loyal Customer 25 Personal Travel Eco
1843 dissatisfied Male Loyal Customer 28 Personal Travel Eco
1849 dissatisfied Male Loyal Customer 38 Personal Travel Eco
1866 dissatisfied Male Loyal Customer 43 Personal Travel Business
1899 satisfied Female Loyal Customer 7 Personal Travel Eco Plus
1901 satisfied Female Loyal Customer 32 Personal Travel Eco
1907 dissatisfied Male Loyal Customer 8 Personal Travel Eco
1934 satisfied Female Loyal Customer 36 Personal Travel Eco
1950 dissatisfied Male Loyal Customer 68 Personal Travel Eco
1961 satisfied Female Loyal Customer 12 Personal Travel Eco
1968 satisfied Female Loyal Customer 15 Personal Travel Eco
1982 dissatisfied Male Loyal Customer 68 Personal Travel Eco
2001 dissatisfied Male Loyal Customer 67 Personal Travel Eco
2008 dissatisfied Male Loyal Customer 10 Personal Travel Eco
2015 satisfied Female Loyal Customer 46 Personal Travel Eco
2030 satisfied Female Loyal Customer 20 Personal Travel Eco
2056 dissatisfied Male Loyal Customer 54 Personal Travel Eco
2069 dissatisfied Male Loyal Customer 21 Personal Travel Eco
2086 dissatisfied Male Loyal Customer 22 Personal Travel Eco
2100 dissatisfied Female Loyal Customer 64 Personal Travel Eco
2112 satisfied Female Loyal Customer 50 Personal Travel Business
2115 dissatisfied Male Loyal Customer 60 Personal Travel Eco
2126 dissatisfied Male Loyal Customer 66 Personal Travel Eco
2138 dissatisfied Male Loyal Customer 65 Personal Travel Eco
2139 dissatisfied Male Loyal Customer 15 Personal Travel Eco
2173 dissatisfied Male Loyal Customer 61 Personal Travel Eco
2188 satisfied Female Loyal Customer 10 Personal Travel Eco
2207 dissatisfied Male Loyal Customer 9 Personal Travel Eco
2209 dissatisfied Male Loyal Customer 11 Personal Travel Eco
2224 satisfied Female Loyal Customer 38 Personal Travel Eco
2246 dissatisfied Male Loyal Customer 55 Personal Travel Eco Plus
2249 satisfied Female Loyal Customer 55 Personal Travel Eco
2287 satisfied Female Loyal Customer 30 Personal Travel Eco
2292 satisfied Female Loyal Customer 19 Personal Travel Eco
2295 satisfied Female Loyal Customer 65 Personal Travel Eco
2297 dissatisfied Male Loyal Customer 50 Personal Travel Business
2317 satisfied Female Loyal Customer 32 Personal Travel Eco
2372 dissatisfied Male Loyal Customer 31 Personal Travel Eco
2387 dissatisfied Male Loyal Customer 23 Personal Travel Eco
2401 satisfied Female Loyal Customer 24 Personal Travel Eco
2408 dissatisfied Male Loyal Customer 29 Personal Travel Eco Plus
2411 satisfied Female Loyal Customer 45 Personal Travel Eco
2416 satisfied Female Loyal Customer 41 Personal Travel Eco
2442 dissatisfied Female Loyal Customer 66 Personal Travel Eco
2456 satisfied Female Loyal Customer 16 Personal Travel Eco
2464 satisfied Female Loyal Customer 23 Personal Travel Eco
2516 dissatisfied Male Loyal Customer 54 Personal Travel Eco
2531 dissatisfied Male Loyal Customer 69 Personal Travel Eco
2551 satisfied Female Loyal Customer 28 Personal Travel Eco
2553 satisfied Female Loyal Customer 53 Personal Travel Eco Plus
2555 satisfied Female Loyal Customer 49 Personal Travel Eco
2564 dissatisfied Male Loyal Customer 11 Personal Travel Eco
2583 satisfied Female Loyal Customer 52 Personal Travel Eco
2598 dissatisfied Male Loyal Customer 55 Personal Travel Eco
2599 satisfied Female Loyal Customer 24 Personal Travel Eco
2606 satisfied Female Loyal Customer 9 Personal Travel Eco
2648 satisfied Female Loyal Customer 34 Personal Travel Eco
2655 dissatisfied Male Loyal Customer 47 Personal Travel Eco
2668 dissatisfied Male Loyal Customer 15 Personal Travel Eco Plus
2713 satisfied Female Loyal Customer 19 Personal Travel Eco
2717 satisfied Female Loyal Customer 57 Personal Travel Eco
2732 satisfied Female Loyal Customer 45 Personal Travel Eco
2739 dissatisfied Male Loyal Customer 13 Personal Travel Business
2756 satisfied Female Loyal Customer 34 Personal Travel Eco
2760 dissatisfied Male Loyal Customer 27 Personal Travel Eco
2764 dissatisfied Male Loyal Customer 45 Personal Travel Eco Plus
2767 dissatisfied Male Loyal Customer 8 Personal Travel Eco
2774 satisfied Female Loyal Customer 31 Personal Travel Eco Plus
2792 satisfied Female Loyal Customer 19 Personal Travel Eco
2795 dissatisfied Male Loyal Customer 7 Personal Travel Eco
2813 dissatisfied Male Loyal Customer 38 Personal Travel Eco
2815 satisfied Female Loyal Customer 69 Personal Travel Eco
2841 dissatisfied Male Loyal Customer 32 Personal Travel Eco
2865 satisfied Female Loyal Customer 55 Personal Travel Eco
2874 dissatisfied Male Loyal Customer 37 Personal Travel Eco
2875 dissatisfied Male Loyal Customer 42 Personal Travel Eco
2892 dissatisfied Male Loyal Customer 29 Personal Travel Eco
2908 dissatisfied Male Loyal Customer 42 Personal Travel Eco
2910 dissatisfied Male Loyal Customer 18 Personal Travel Eco
2917 satisfied Female Loyal Customer 69 Personal Travel Eco
2933 dissatisfied Male Loyal Customer 37 Personal Travel Eco
2938 dissatisfied Male Loyal Customer 29 Personal Travel Eco
2941 satisfied Female Loyal Customer 40 Personal Travel Eco
2945 satisfied Female Loyal Customer 54 Personal Travel Eco
2946 dissatisfied Male Loyal Customer 56 Personal Travel Eco
2967 dissatisfied Male Loyal Customer 45 Personal Travel Eco
2993 dissatisfied Male Loyal Customer 69 Personal Travel Eco
3004 dissatisfied Male Loyal Customer 48 Personal Travel Eco
3007 satisfied Female Loyal Customer 56 Personal Travel Eco
3023 satisfied Female Loyal Customer 17 Personal Travel Eco
3026 satisfied Female Loyal Customer 61 Personal Travel Eco
3029 satisfied Female Loyal Customer 41 Personal Travel Business
3035 dissatisfied Male Loyal Customer 42 Personal Travel Eco
3037 satisfied Female Loyal Customer 32 Personal Travel Eco Plus
3055 satisfied Female Loyal Customer 33 Personal Travel Eco
3057 dissatisfied Male Loyal Customer 62 Personal Travel Eco
3069 dissatisfied Male Loyal Customer 46 Personal Travel Eco
3077 dissatisfied Male Loyal Customer 12 Personal Travel Eco
3080 satisfied Female Loyal Customer 58 Personal Travel Business
3082 satisfied Female Loyal Customer 22 Personal Travel Eco
3095 satisfied Female Loyal Customer 33 Personal Travel Eco
3121 dissatisfied Male Loyal Customer 63 Personal Travel Eco
3128 dissatisfied Male Loyal Customer 51 Personal Travel Eco
3133 dissatisfied Male Loyal Customer 54 Personal Travel Eco
3150 satisfied Female Loyal Customer 65 Personal Travel Eco
3153 satisfied Female Loyal Customer 69 Personal Travel Eco
3156 dissatisfied Male Loyal Customer 40 Personal Travel Eco
3167 dissatisfied Male Loyal Customer 11 Personal Travel Business
3184 satisfied Female Loyal Customer 40 Personal Travel Eco
3186 satisfied Female Loyal Customer 57 Personal Travel Eco
3199 satisfied Female Loyal Customer 21 Personal Travel Eco Plus
3202 satisfied Female Loyal Customer 35 Personal Travel Eco
3220 satisfied Female Loyal Customer 38 Personal Travel Eco
3248 dissatisfied Male Loyal Customer 17 Personal Travel Eco
3251 satisfied Female Loyal Customer 23 Personal Travel Eco
3254 satisfied Female Loyal Customer 46 Personal Travel Eco
3259 satisfied Female Loyal Customer 39 Personal Travel Eco
3262 satisfied Female Loyal Customer 56 Personal Travel Eco
3292 dissatisfied Male Loyal Customer 55 Personal Travel Eco
3297 satisfied Female Loyal Customer 61 Personal Travel Eco
3305 dissatisfied Male Loyal Customer 58 Personal Travel Eco
3310 satisfied Female Loyal Customer 21 Personal Travel Eco
3318 dissatisfied Male Loyal Customer 52 Personal Travel Eco
3319 dissatisfied Male Loyal Customer 59 Personal Travel Eco
3331 satisfied Female Loyal Customer 14 Personal Travel Eco
3332 satisfied Female Loyal Customer 67 Personal Travel Eco Plus
3365 satisfied Female Loyal Customer 70 Personal Travel Eco
3368 dissatisfied Male Loyal Customer 44 Personal Travel Eco
3394 dissatisfied Male Loyal Customer 65 Personal Travel Eco
3406 satisfied Female Loyal Customer 36 Personal Travel Eco
3434 satisfied Female Loyal Customer 17 Personal Travel Business
3444 satisfied Female Loyal Customer 52 Personal Travel Eco Plus
3454 satisfied Female Loyal Customer 55 Personal Travel Eco
3459 dissatisfied Male Loyal Customer 69 Personal Travel Eco
3471 dissatisfied Female Loyal Customer 46 Personal Travel Eco
3480 satisfied Female Loyal Customer 58 Personal Travel Eco
3507 dissatisfied Male Loyal Customer 69 Personal Travel Eco
3515 satisfied Female Loyal Customer 12 Personal Travel Eco
3542 satisfied Female Loyal Customer 67 Personal Travel Eco
3551 dissatisfied Male Loyal Customer 23 Personal Travel Eco
3552 satisfied Female Loyal Customer 40 Personal Travel Eco
3580 satisfied Female Loyal Customer 22 Personal Travel Eco
3612 dissatisfied Male Loyal Customer 32 Personal Travel Eco Plus
3629 satisfied Female Loyal Customer 39 Personal Travel Eco
3630 satisfied Female Loyal Customer 53 Personal Travel Eco
3663 satisfied Female Loyal Customer 51 Personal Travel Eco
3668 satisfied Female Loyal Customer 42 Personal Travel Eco
3670 satisfied Female Loyal Customer 54 Personal Travel Eco
3690 dissatisfied Male Loyal Customer 9 Personal Travel Eco
3691 satisfied Female Loyal Customer 7 Personal Travel Eco
3711 satisfied Female Loyal Customer 7 Personal Travel Eco Plus
3732 satisfied Female Loyal Customer 65 Personal Travel Eco
3746 satisfied Female Loyal Customer 26 Personal Travel Eco
3747 dissatisfied Male Loyal Customer 69 Personal Travel Eco
3754 dissatisfied Male Loyal Customer 36 Personal Travel Eco Plus
3761 dissatisfied Male Loyal Customer 18 Personal Travel Eco
3808 satisfied Female Loyal Customer 23 Personal Travel Eco
3811 satisfied Female Loyal Customer 67 Personal Travel Eco
3820 dissatisfied Male Loyal Customer 51 Personal Travel Eco
3834 dissatisfied Male Loyal Customer 59 Personal Travel Eco
3841 dissatisfied Male Loyal Customer 12 Personal Travel Eco
3853 dissatisfied Male Loyal Customer 55 Personal Travel Eco
3864 satisfied Female Loyal Customer 59 Personal Travel Eco
3871 dissatisfied Male Loyal Customer 13 Personal Travel Eco
3880 dissatisfied Female Loyal Customer 16 Personal Travel Eco
3882 dissatisfied Male Loyal Customer 66 Personal Travel Eco
3904 satisfied Female Loyal Customer 18 Personal Travel Eco
3911 dissatisfied Male Loyal Customer 53 Personal Travel Eco
3933 satisfied Female Loyal Customer 52 Personal Travel Eco
3948 dissatisfied Male Loyal Customer 40 Personal Travel Eco
3954 satisfied Female Loyal Customer 68 Personal Travel Eco
3972 dissatisfied Male Loyal Customer 55 Personal Travel Eco
3975 dissatisfied Male Loyal Customer 42 Personal Travel Eco
3988 satisfied Female Loyal Customer 22 Personal Travel Eco
3993 satisfied Female Loyal Customer 50 Personal Travel Eco
4000 dissatisfied Male Loyal Customer 42 Personal Travel Eco
4010 satisfied Female Loyal Customer 51 Personal Travel Eco
4027 dissatisfied Male Loyal Customer 64 Personal Travel Eco
4043 dissatisfied Male Loyal Customer 38 Personal Travel Eco
4051 dissatisfied Male Loyal Customer 60 Personal Travel Eco
4053 satisfied Female Loyal Customer 41 Personal Travel Eco Plus
4055 dissatisfied Male Loyal Customer 43 Personal Travel Eco
4080 dissatisfied Male Loyal Customer 40 Personal Travel Eco Plus
4082 satisfied Female Loyal Customer 21 Personal Travel Eco
4090 satisfied Female Loyal Customer 17 Personal Travel Eco
4091 satisfied Female Loyal Customer 20 Personal Travel Eco
4116 dissatisfied Male Loyal Customer 62 Personal Travel Eco
4208 satisfied Female Loyal Customer 39 Personal Travel Business
4209 dissatisfied Male Loyal Customer 44 Personal Travel Eco
4210 satisfied Female Loyal Customer 60 Personal Travel Eco
4233 satisfied Female Loyal Customer 46 Personal Travel Eco
4244 dissatisfied Male Loyal Customer 63 Personal Travel Eco
4257 dissatisfied Male Loyal Customer 17 Personal Travel Eco
4260 dissatisfied Male Loyal Customer 31 Personal Travel Eco
4262 dissatisfied Male Loyal Customer 61 Personal Travel Eco
4264 dissatisfied Male Loyal Customer 24 Personal Travel Eco Plus
4318 satisfied Female Loyal Customer 20 Personal Travel Eco
4335 dissatisfied Male Loyal Customer 64 Personal Travel Eco Plus
4341 satisfied Female Loyal Customer 64 Personal Travel Eco
4354 dissatisfied Male Loyal Customer 46 Personal Travel Eco
4362 satisfied Female Loyal Customer 20 Personal Travel Eco
4393 satisfied Female Loyal Customer 51 Personal Travel Eco
4394 satisfied Female Loyal Customer 7 Personal Travel Eco
4402 satisfied Female Loyal Customer 47 Personal Travel Eco
4416 dissatisfied Male Loyal Customer 57 Personal Travel Eco
4439 satisfied Female Loyal Customer 40 Personal Travel Eco
4440 satisfied Female Loyal Customer 58 Personal Travel Eco
4442 dissatisfied Male Loyal Customer 54 Personal Travel Business
4458 dissatisfied Male Loyal Customer 56 Personal Travel Eco
4488 dissatisfied Male Loyal Customer 18 Personal Travel Eco
4492 dissatisfied Male Loyal Customer 62 Personal Travel Eco
4500 satisfied Female Loyal Customer 21 Personal Travel Eco
4503 satisfied Female Loyal Customer 48 Personal Travel Eco
4582 dissatisfied Male Loyal Customer 64 Personal Travel Business
4586 dissatisfied Male Loyal Customer 57 Personal Travel Eco
4598 satisfied Female Loyal Customer 34 Personal Travel Eco
4601 satisfied Female Loyal Customer 60 Personal Travel Eco
4605 satisfied Female Loyal Customer 60 Personal Travel Eco
4608 dissatisfied Male Loyal Customer 28 Personal Travel Eco
4617 dissatisfied Male Loyal Customer 64 Personal Travel Eco Plus
4627 dissatisfied Male Loyal Customer 69 Personal Travel Eco Plus
4655 dissatisfied Male Loyal Customer 38 Personal Travel Business
4658 dissatisfied Male Loyal Customer 11 Personal Travel Eco
4683 satisfied Female Loyal Customer 55 Personal Travel Eco
4686 dissatisfied Male Loyal Customer 57 Personal Travel Eco
4694 satisfied Female Loyal Customer 15 Personal Travel Eco
4697 satisfied Female Loyal Customer 47 Personal Travel Eco
4722 satisfied Female Loyal Customer 70 Personal Travel Eco
4752 dissatisfied Male Loyal Customer 69 Personal Travel Eco
4756 dissatisfied Male Loyal Customer 27 Personal Travel Eco Plus
4762 satisfied Female Loyal Customer 31 Personal Travel Eco
4783 dissatisfied Male Loyal Customer 25 Personal Travel Eco Plus
4802 satisfied Female Loyal Customer 52 Personal Travel Eco
4806 satisfied Female Loyal Customer 13 Personal Travel Eco
4813 satisfied Female Loyal Customer 45 Personal Travel Eco
4832 dissatisfied Male Loyal Customer 31 Personal Travel Eco
4862 dissatisfied Male Loyal Customer 26 Personal Travel Eco
4870 satisfied Female Loyal Customer 63 Personal Travel Eco
4876 dissatisfied Male Loyal Customer 46 Personal Travel Eco
4879 dissatisfied Male Loyal Customer 62 Personal Travel Eco
4928 dissatisfied Male Loyal Customer 37 Personal Travel Eco
4938 satisfied Female Loyal Customer 22 Personal Travel Eco
4965 dissatisfied Male Loyal Customer 29 Personal Travel Eco
4981 dissatisfied Male Loyal Customer 46 Personal Travel Eco
4983 dissatisfied Male Loyal Customer 31 Personal Travel Eco
5010 satisfied Female Loyal Customer 54 Personal Travel Eco
5044 satisfied Female Loyal Customer 17 Personal Travel Eco
5046 satisfied Female Loyal Customer 20 Personal Travel Eco
5057 dissatisfied Male Loyal Customer 38 Personal Travel Business
5075 dissatisfied Male Loyal Customer 28 Personal Travel Eco
5086 satisfied Female Loyal Customer 49 Personal Travel Eco
5119 dissatisfied Male Loyal Customer 30 Personal Travel Eco
5125 dissatisfied Male Loyal Customer 54 Personal Travel Eco
5148 satisfied Female Loyal Customer 33 Personal Travel Eco
5154 dissatisfied Male Loyal Customer 25 Personal Travel Eco
5162 dissatisfied Male Loyal Customer 66 Personal Travel Eco
5169 dissatisfied Male Loyal Customer 37 Personal Travel Eco Plus
5180 satisfied Female Loyal Customer 65 Personal Travel Eco
5224 dissatisfied Male Loyal Customer 60 Personal Travel Eco
5266 satisfied Female Loyal Customer 34 Personal Travel Eco
5295 dissatisfied Male Loyal Customer 18 Personal Travel Eco
5326 dissatisfied Male Loyal Customer 42 Personal Travel Eco
5330 dissatisfied Male Loyal Customer 49 Personal Travel Eco
5331 dissatisfied Male Loyal Customer 48 Personal Travel Eco
5341 satisfied Female Loyal Customer 32 Personal Travel Eco
5382 dissatisfied Male Loyal Customer 12 Personal Travel Eco
5387 satisfied Female Loyal Customer 47 Personal Travel Eco
5403 dissatisfied Male Loyal Customer 47 Personal Travel Eco
5433 satisfied Female Loyal Customer 17 Personal Travel Eco
5438 dissatisfied Male Loyal Customer 35 Personal Travel Eco
5452 dissatisfied Male Loyal Customer 46 Personal Travel Eco
5464 dissatisfied Male Loyal Customer 34 Personal Travel Eco
5477 dissatisfied Female Loyal Customer 7 Personal Travel Eco
5478 satisfied Female Loyal Customer 33 Personal Travel Eco
5486 dissatisfied Male Loyal Customer 52 Personal Travel Eco
5497 dissatisfied Male Loyal Customer 63 Personal Travel Eco
5523 dissatisfied Male Loyal Customer 7 Personal Travel Eco
5533 dissatisfied Male Loyal Customer 63 Personal Travel Eco
5543 dissatisfied Male Loyal Customer 40 Personal Travel Eco
5549 dissatisfied Male Loyal Customer 47 Personal Travel Eco
5551 satisfied Female Loyal Customer 63 Personal Travel Eco
5556 satisfied Female Loyal Customer 12 Personal Travel Eco
5577 dissatisfied Male Loyal Customer 43 Personal Travel Eco
5588 dissatisfied Male Loyal Customer 52 Personal Travel Eco
5589 satisfied Female Loyal Customer 8 Personal Travel Eco Plus
5622 dissatisfied Female Loyal Customer 47 Personal Travel Eco
5627 satisfied Female Loyal Customer 48 Personal Travel Eco
5654 satisfied Female Loyal Customer 33 Personal Travel Eco
5662 satisfied Female Loyal Customer 44 Personal Travel Eco
5675 dissatisfied Male Loyal Customer 19 Personal Travel Eco
5693 dissatisfied Male Loyal Customer 63 Personal Travel Eco
5694 dissatisfied Male Loyal Customer 57 Personal Travel Eco
5699 satisfied Female Loyal Customer 27 Personal Travel Eco
5710 dissatisfied Male Loyal Customer 18 Personal Travel Eco
5712 dissatisfied Male Loyal Customer 9 Personal Travel Eco
5716 satisfied Female Loyal Customer 47 Personal Travel Business
5734 satisfied Female Loyal Customer 40 Personal Travel Eco
5739 dissatisfied Male Loyal Customer 17 Personal Travel Eco
5758 satisfied Female Loyal Customer 15 Personal Travel Business
5760 satisfied Female Loyal Customer 13 Personal Travel Eco
5762 dissatisfied Male Loyal Customer 48 Personal Travel Eco
5788 satisfied Female Loyal Customer 28 Personal Travel Eco
5797 satisfied Female Loyal Customer 22 Personal Travel Eco
5829 satisfied Female Loyal Customer 17 Personal Travel Eco
5833 dissatisfied Male Loyal Customer 32 Personal Travel Eco
5837 satisfied Female Loyal Customer 52 Personal Travel Eco
5847 satisfied Female Loyal Customer 53 Personal Travel Eco
5888 satisfied Female Loyal Customer 63 Personal Travel Business
5915 dissatisfied Male Loyal Customer 29 Personal Travel Eco
5917 satisfied Female Loyal Customer 68 Personal Travel Eco
5931 satisfied Female Loyal Customer 55 Personal Travel Eco
5945 dissatisfied Male Loyal Customer 12 Personal Travel Eco
5949 satisfied Female Loyal Customer 29 Personal Travel Eco
5953 dissatisfied Male Loyal Customer 53 Personal Travel Eco
5977 satisfied Female Loyal Customer 7 Personal Travel Eco
5984 dissatisfied Male Loyal Customer 52 Personal Travel Eco
5989 dissatisfied Male Loyal Customer 24 Personal Travel Eco
5998 dissatisfied Male Loyal Customer 70 Personal Travel Eco
5999 satisfied Female Loyal Customer 56 Personal Travel Eco
6002 satisfied Female Loyal Customer 17 Personal Travel Eco
6018 satisfied Female Loyal Customer 47 Personal Travel Eco
6019 satisfied Female Loyal Customer 68 Personal Travel Eco
6027 satisfied Female Loyal Customer 18 Personal Travel Eco
6071 satisfied Female Loyal Customer 55 Personal Travel Eco
6081 dissatisfied Male Loyal Customer 48 Personal Travel Eco
6097 dissatisfied Male Loyal Customer 51 Personal Travel Eco
6101 dissatisfied Male Loyal Customer 37 Personal Travel Eco
6112 satisfied Female Loyal Customer 39 Personal Travel Eco
6127 satisfied Female Loyal Customer 51 Personal Travel Eco
6146 dissatisfied Male Loyal Customer 49 Personal Travel Eco
6156 dissatisfied Male Loyal Customer 8 Personal Travel Eco
6159 dissatisfied Male Loyal Customer 25 Personal Travel Eco Plus
6160 satisfied Female Loyal Customer 25 Personal Travel Eco
6182 dissatisfied Male Loyal Customer 8 Personal Travel Eco
6186 satisfied Female Loyal Customer 54 Personal Travel Eco Plus
6189 satisfied Female Loyal Customer 10 Personal Travel Eco
6200 dissatisfied Male Loyal Customer 56 Personal Travel Business
6202 dissatisfied Male Loyal Customer 24 Personal Travel Eco
6214 dissatisfied Male Loyal Customer 33 Personal Travel Eco Plus
6216 satisfied Female Loyal Customer 27 Personal Travel Eco
6217 satisfied Female Loyal Customer 7 Personal Travel Eco
6231 satisfied Female Loyal Customer 33 Personal Travel Eco
6239 dissatisfied Male Loyal Customer 65 Personal Travel Eco Plus
6257 dissatisfied Male Loyal Customer 70 Personal Travel Eco
6267 dissatisfied Male Loyal Customer 9 Personal Travel Eco
6269 dissatisfied Male Loyal Customer 44 Personal Travel Eco
6274 dissatisfied Male Loyal Customer 41 Personal Travel Eco
6280 dissatisfied Male Loyal Customer 20 Personal Travel Eco
6287 dissatisfied Male Loyal Customer 31 Personal Travel Eco
6309 dissatisfied Male Loyal Customer 24 Personal Travel Eco
6360 satisfied Female Loyal Customer 10 Personal Travel Eco
6365 dissatisfied Male Loyal Customer 29 Personal Travel Eco
6382 satisfied Female Loyal Customer 28 Personal Travel Eco
6393 satisfied Female Loyal Customer 70 Personal Travel Eco Plus
6416 dissatisfied Male Loyal Customer 45 Personal Travel Eco Plus
6452 satisfied Female Loyal Customer 37 Personal Travel Eco
6461 satisfied Female Loyal Customer 12 Personal Travel Eco
6523 dissatisfied Male Loyal Customer 32 Personal Travel Eco
6529 dissatisfied Male Loyal Customer 65 Personal Travel Eco
6540 satisfied Female Loyal Customer 62 Personal Travel Business
6541 satisfied Female Loyal Customer 38 Personal Travel Eco
6547 dissatisfied Male Loyal Customer 70 Personal Travel Eco
6552 satisfied Female Loyal Customer 16 Personal Travel Eco
6588 satisfied Female Loyal Customer 29 Personal Travel Eco
6591 dissatisfied Male Loyal Customer 41 Personal Travel Eco
6625 dissatisfied Male Loyal Customer 42 Personal Travel Eco
6636 satisfied Female Loyal Customer 10 Personal Travel Eco
6665 dissatisfied Male Loyal Customer 20 Personal Travel Eco
6667 dissatisfied Male Loyal Customer 60 Personal Travel Eco
6684 satisfied Female Loyal Customer 15 Personal Travel Eco
6689 dissatisfied Male Loyal Customer 26 Personal Travel Eco
6691 dissatisfied Male Loyal Customer 67 Personal Travel Eco Plus
6707 satisfied Female Loyal Customer 51 Personal Travel Eco
6725 dissatisfied Male Loyal Customer 69 Personal Travel Eco
6728 dissatisfied Male Loyal Customer 34 Personal Travel Business
6745 satisfied Female Loyal Customer 37 Personal Travel Eco
6752 dissatisfied Male Loyal Customer 65 Personal Travel Eco
6753 dissatisfied Male Loyal Customer 37 Personal Travel Eco
6758 satisfied Female Loyal Customer 36 Personal Travel Eco
6766 satisfied Female Loyal Customer 60 Personal Travel Eco
6780 dissatisfied Male Loyal Customer 41 Personal Travel Eco Plus
6790 dissatisfied Male Loyal Customer 53 Personal Travel Eco
6810 satisfied Female Loyal Customer 68 Personal Travel Eco
6815 dissatisfied Male Loyal Customer 45 Personal Travel Eco
6825 dissatisfied Male Loyal Customer 31 Personal Travel Eco
6842 dissatisfied Male Loyal Customer 62 Personal Travel Eco
6843 satisfied Female Loyal Customer 59 Personal Travel Eco
6854 satisfied Female Loyal Customer 27 Personal Travel Eco
6871 satisfied Female Loyal Customer 15 Personal Travel Business
6874 dissatisfied Male Loyal Customer 60 Personal Travel Eco
6879 dissatisfied Male Loyal Customer 40 Personal Travel Eco Plus
6880 satisfied Female Loyal Customer 57 Personal Travel Eco
6891 satisfied Female Loyal Customer 19 Personal Travel Eco Plus
6892 satisfied Female Loyal Customer 68 Personal Travel Eco
6918 satisfied Female Loyal Customer 39 Personal Travel Eco Plus
6938 dissatisfied Male Loyal Customer 44 Personal Travel Eco
6940 dissatisfied Male Loyal Customer 41 Personal Travel Eco
6955 dissatisfied Male Loyal Customer 70 Personal Travel Eco Plus
6972 satisfied Female Loyal Customer 56 Personal Travel Eco
6974 satisfied Female Loyal Customer 20 Personal Travel Eco
6983 dissatisfied Male Loyal Customer 39 Personal Travel Eco
6993 dissatisfied Male Loyal Customer 33 Personal Travel Eco
7004 satisfied Female Loyal Customer 18 Personal Travel Business
7008 dissatisfied Male Loyal Customer 47 Personal Travel Eco
7034 dissatisfied Male Loyal Customer 36 Personal Travel Eco
7065 satisfied Female Loyal Customer 9 Personal Travel Eco
7069 satisfied Female Loyal Customer 39 Personal Travel Eco
7070 satisfied Female Loyal Customer 29 Personal Travel Eco
7072 dissatisfied Male Loyal Customer 50 Personal Travel Eco
7082 satisfied Female Loyal Customer 23 Personal Travel Eco
7090 dissatisfied Male Loyal Customer 8 Personal Travel Business
7152 dissatisfied Male Loyal Customer 62 Personal Travel Eco
7154 satisfied Female Loyal Customer 65 Personal Travel Eco
7172 satisfied Female Loyal Customer 66 Personal Travel Eco
7179 dissatisfied Male Loyal Customer 42 Personal Travel Eco Plus
7187 satisfied Female Loyal Customer 13 Personal Travel Eco
7198 satisfied Female Loyal Customer 29 Personal Travel Eco
7202 dissatisfied Male Loyal Customer 24 Personal Travel Eco
7204 satisfied Female Loyal Customer 63 Personal Travel Eco
7216 dissatisfied Male Loyal Customer 41 Personal Travel Eco
7218 dissatisfied Male Loyal Customer 23 Personal Travel Eco
7231 satisfied Female Loyal Customer 38 Personal Travel Eco
7267 dissatisfied Male Loyal Customer 50 Personal Travel Eco
7382 dissatisfied Male Loyal Customer 54 Personal Travel Eco
7393 satisfied Female Loyal Customer 17 Personal Travel Eco
7408 satisfied Female Loyal Customer 9 Personal Travel Eco Plus
7431 dissatisfied Male Loyal Customer 63 Personal Travel Eco Plus
7437 satisfied Female Loyal Customer 34 Personal Travel Eco
7438 satisfied Female Loyal Customer 57 Personal Travel Eco
7443 satisfied Female Loyal Customer 58 Personal Travel Business
7480 satisfied Female Loyal Customer 44 Personal Travel Eco
7482 dissatisfied Female Loyal Customer 39 Personal Travel Eco Plus
7497 satisfied Female Loyal Customer 51 Personal Travel Eco
7518 satisfied Female Loyal Customer 17 Personal Travel Eco
7519 satisfied Female Loyal Customer 49 Personal Travel Eco
7531 satisfied Female Loyal Customer 8 Personal Travel Eco
7538 satisfied Female Loyal Customer 68 Personal Travel Eco Plus
7574 satisfied Female Loyal Customer 69 Personal Travel Eco
7592 dissatisfied Male Loyal Customer 35 Personal Travel Eco
7607 satisfied Female Loyal Customer 22 Personal Travel Eco Plus
7609 dissatisfied Male Loyal Customer 14 Personal Travel Eco
7611 satisfied Female Loyal Customer 63 Personal Travel Eco
7612 dissatisfied Male Loyal Customer 17 Personal Travel Business
7644 satisfied Female Loyal Customer 63 Personal Travel Eco Plus
7653 satisfied Female Loyal Customer 36 Personal Travel Business
7660 satisfied Female Loyal Customer 34 Personal Travel Eco
7671 satisfied Female Loyal Customer 7 Personal Travel Eco
7687 dissatisfied Male Loyal Customer 60 Personal Travel Eco
7696 dissatisfied Male Loyal Customer 34 Personal Travel Eco
7709 dissatisfied Male Loyal Customer 27 Personal Travel Eco
7718 satisfied Female Loyal Customer 26 Personal Travel Eco
7734 dissatisfied Male Loyal Customer 48 Personal Travel Eco
7758 dissatisfied Male Loyal Customer 13 Personal Travel Eco Plus
7760 dissatisfied Male Loyal Customer 30 Personal Travel Eco Plus
7761 dissatisfied Male Loyal Customer 11 Personal Travel Eco
7779 dissatisfied Male Loyal Customer 52 Personal Travel Business
7797 satisfied Female Loyal Customer 28 Personal Travel Eco
7801 dissatisfied Male Loyal Customer 14 Personal Travel Eco
7804 dissatisfied Male Loyal Customer 12 Personal Travel Eco
7805 satisfied Female Loyal Customer 66 Personal Travel Eco
7807 satisfied Female Loyal Customer 15 Personal Travel Eco Plus
7808 dissatisfied Male Loyal Customer 51 Personal Travel Eco
7823 dissatisfied Male Loyal Customer 44 Personal Travel Eco
7829 satisfied Female Loyal Customer 29 Personal Travel Eco
7845 satisfied Female Loyal Customer 7 Personal Travel Eco
7849 dissatisfied Male Loyal Customer 47 Personal Travel Eco
7868 satisfied Female Loyal Customer 47 Personal Travel Eco
7902 satisfied Female Loyal Customer 39 Personal Travel Eco
7918 dissatisfied Male Loyal Customer 24 Personal Travel Eco
7922 satisfied Female Loyal Customer 23 Personal Travel Eco
7938 satisfied Female Loyal Customer 49 Personal Travel Eco
7944 satisfied Female Loyal Customer 51 Personal Travel Eco
7947 dissatisfied Male Loyal Customer 27 Personal Travel Eco
7953 dissatisfied Male Loyal Customer 31 Personal Travel Eco Plus
8012 satisfied Female Loyal Customer 61 Personal Travel Eco Plus
8032 satisfied Female Loyal Customer 14 Personal Travel Eco
8041 satisfied Female Loyal Customer 69 Personal Travel Eco
8050 dissatisfied Male Loyal Customer 58 Personal Travel Eco
8061 dissatisfied Male Loyal Customer 28 Personal Travel Eco
8077 dissatisfied Male Loyal Customer 20 Personal Travel Eco Plus
8081 dissatisfied Male Loyal Customer 24 Personal Travel Eco
8108 dissatisfied Male Loyal Customer 41 Personal Travel Eco
8125 dissatisfied Male Loyal Customer 52 Personal Travel Eco
8130 satisfied Female Loyal Customer 36 Personal Travel Eco
8157 dissatisfied Male Loyal Customer 10 Personal Travel Eco
8166 satisfied Female Loyal Customer 34 Personal Travel Eco
8169 dissatisfied Male Loyal Customer 52 Personal Travel Eco Plus
8180 satisfied Female Loyal Customer 50 Personal Travel Eco Plus
8185 satisfied Female Loyal Customer 46 Personal Travel Eco
8186 satisfied Female Loyal Customer 63 Personal Travel Eco Plus
8191 dissatisfied Female Loyal Customer 38 Personal Travel Eco
8217 dissatisfied Male Loyal Customer 61 Personal Travel Eco
8218 dissatisfied Male Loyal Customer 54 Personal Travel Eco
8228 satisfied Female Loyal Customer 24 Personal Travel Eco
8253 satisfied Female Loyal Customer 27 Personal Travel Eco
8271 dissatisfied Male Loyal Customer 62 Personal Travel Eco Plus
8313 dissatisfied Male Loyal Customer 44 Personal Travel Business
8345 dissatisfied Male Loyal Customer 43 Personal Travel Eco
8346 dissatisfied Male Loyal Customer 22 Personal Travel Eco
8356 satisfied Female Loyal Customer 29 Personal Travel Eco
8361 dissatisfied Male Loyal Customer 39 Personal Travel Eco
8362 dissatisfied Male Loyal Customer 36 Personal Travel Eco
8368 dissatisfied Male Loyal Customer 25 Personal Travel Eco
8422 satisfied Female Loyal Customer 53 Personal Travel Eco Plus
8429 dissatisfied Male Loyal Customer 23 Personal Travel Eco
8436 dissatisfied Male Loyal Customer 59 Personal Travel Eco
8454 dissatisfied Male Loyal Customer 47 Personal Travel Eco
8473 satisfied Female Loyal Customer 48 Personal Travel Eco
8489 dissatisfied Male Loyal Customer 38 Personal Travel Eco
8517 satisfied Female Loyal Customer 11 Personal Travel Eco
8522 satisfied Female Loyal Customer 16 Personal Travel Eco Plus
8530 dissatisfied Male Loyal Customer 68 Personal Travel Eco
8532 dissatisfied Male Loyal Customer 47 Personal Travel Eco
8548 dissatisfied Male Loyal Customer 53 Personal Travel Eco
8551 satisfied Female Loyal Customer 28 Personal Travel Eco
8552 dissatisfied Male Loyal Customer 45 Personal Travel Eco
8567 satisfied Female Loyal Customer 17 Personal Travel Eco
8584 dissatisfied Male Loyal Customer 36 Personal Travel Eco
8606 satisfied Female Loyal Customer 21 Personal Travel Eco
8612 satisfied Female Loyal Customer 53 Personal Travel Eco
8618 satisfied Female Loyal Customer 34 Personal Travel Eco
8639 dissatisfied Male Loyal Customer 48 Personal Travel Eco Plus
8650 satisfied Female Loyal Customer 65 Personal Travel Eco
8657 satisfied Female Loyal Customer 42 Personal Travel Eco
8716 satisfied Female Loyal Customer 31 Personal Travel Eco Plus
8720 satisfied Female Loyal Customer 57 Personal Travel Eco
8741 dissatisfied Male Loyal Customer 48 Personal Travel Eco Plus
8742 dissatisfied Male Loyal Customer 45 Personal Travel Business
8750 satisfied Female Loyal Customer 30 Personal Travel Eco
8758 dissatisfied Male Loyal Customer 7 Personal Travel Eco
8804 dissatisfied Male Loyal Customer 7 Personal Travel Eco
8807 satisfied Female Loyal Customer 12 Personal Travel Eco
8811 satisfied Female Loyal Customer 64 Personal Travel Eco
8813 dissatisfied Male Loyal Customer 54 Personal Travel Eco
8825 satisfied Female Loyal Customer 14 Personal Travel Eco Plus
8828 satisfied Female Loyal Customer 47 Personal Travel Eco
8831 satisfied Female Loyal Customer 18 Personal Travel Eco
8834 satisfied Female Loyal Customer 69 Personal Travel Eco
8838 dissatisfied Male Loyal Customer 51 Personal Travel Eco
8853 dissatisfied Male Loyal Customer 62 Personal Travel Eco
8857 dissatisfied Male Loyal Customer 63 Personal Travel Eco
8877 dissatisfied Male Loyal Customer 12 Personal Travel Eco
8892 dissatisfied Male Loyal Customer 55 Personal Travel Eco
8896 dissatisfied Male Loyal Customer 53 Personal Travel Eco
8909 dissatisfied Male Loyal Customer 22 Personal Travel Eco Plus
8925 dissatisfied Male Loyal Customer 51 Personal Travel Eco
8926 satisfied Female Loyal Customer 29 Personal Travel Eco
8930 dissatisfied Male Loyal Customer 37 Personal Travel Eco
8931 satisfied Female Loyal Customer 25 Personal Travel Eco Plus
8939 dissatisfied Male Loyal Customer 32 Personal Travel Eco
8977 satisfied Female Loyal Customer 16 Personal Travel Eco
8988 dissatisfied Male Loyal Customer 49 Personal Travel Eco
9006 dissatisfied Male Loyal Customer 25 Personal Travel Eco
9025 dissatisfied Male Loyal Customer 41 Personal Travel Eco
9029 satisfied Female Loyal Customer 9 Personal Travel Eco
9068 dissatisfied Male Loyal Customer 13 Personal Travel Eco
9070 dissatisfied Male Loyal Customer 39 Personal Travel Eco Plus
9113 dissatisfied Male Loyal Customer 25 Personal Travel Eco
9125 dissatisfied Male Loyal Customer 62 Personal Travel Eco
9129 dissatisfied Male Loyal Customer 47 Personal Travel Eco
9136 dissatisfied Male Loyal Customer 49 Personal Travel Business
9159 dissatisfied Male Loyal Customer 46 Personal Travel Eco
9163 satisfied Female Loyal Customer 18 Personal Travel Business
9185 dissatisfied Male Loyal Customer 40 Personal Travel Eco
9194 dissatisfied Male Loyal Customer 45 Personal Travel Eco Plus
9208 dissatisfied Male Loyal Customer 48 Personal Travel Eco
9212 satisfied Female Loyal Customer 51 Personal Travel Eco
9221 satisfied Female Loyal Customer 11 Personal Travel Eco
9230 satisfied Female Loyal Customer 14 Personal Travel Eco
9231 dissatisfied Male Loyal Customer 27 Personal Travel Eco
9254 satisfied Female Loyal Customer 15 Personal Travel Eco
9262 satisfied Female Loyal Customer 30 Personal Travel Eco
9265 dissatisfied Male Loyal Customer 40 Personal Travel Eco
9268 satisfied Female Loyal Customer 48 Personal Travel Eco Plus
9285 satisfied Female Loyal Customer 44 Personal Travel Eco
9297 satisfied Female Loyal Customer 9 Personal Travel Eco
9298 satisfied Female Loyal Customer 65 Personal Travel Eco
9299 dissatisfied Male Loyal Customer 30 Personal Travel Eco
9304 dissatisfied Male Loyal Customer 12 Personal Travel Eco
9307 dissatisfied Male Loyal Customer 28 Personal Travel Eco Plus
9308 satisfied Female Loyal Customer 17 Personal Travel Eco
9310 satisfied Female Loyal Customer 44 Personal Travel Eco
9314 satisfied Female Loyal Customer 34 Personal Travel Eco
9318 satisfied Female Loyal Customer 66 Personal Travel Eco
9343 dissatisfied Male Loyal Customer 31 Personal Travel Eco
9370 satisfied Female Loyal Customer 51 Personal Travel Eco
9382 dissatisfied Male Loyal Customer 14 Personal Travel Eco
9389 satisfied Female Loyal Customer 36 Personal Travel Business
9403 satisfied Female Loyal Customer 23 Personal Travel Eco
9419 dissatisfied Male Loyal Customer 20 Personal Travel Eco
9431 satisfied Female Loyal Customer 15 Personal Travel Eco
9439 satisfied Female Loyal Customer 16 Personal Travel Eco
9450 satisfied Female Loyal Customer 28 Personal Travel Eco
9451 dissatisfied Male Loyal Customer 36 Personal Travel Business
9467 satisfied Female Loyal Customer 40 Personal Travel Eco
9471 dissatisfied Male Loyal Customer 11 Personal Travel Eco
9475 satisfied Female Loyal Customer 27 Personal Travel Eco
9494 dissatisfied Male Loyal Customer 67 Personal Travel Eco
9502 satisfied Female Loyal Customer 24 Personal Travel Eco
9530 dissatisfied Male Loyal Customer 29 Personal Travel Eco
9556 satisfied Female Loyal Customer 43 Personal Travel Eco
9560 satisfied Female Loyal Customer 39 Personal Travel Eco
9567 dissatisfied Male Loyal Customer 10 Personal Travel Eco
9573 dissatisfied Male Loyal Customer 24 Personal Travel Eco
9576 satisfied Female Loyal Customer 52 Personal Travel Eco
9589 satisfied Female Loyal Customer 44 Personal Travel Eco
9595 dissatisfied Male Loyal Customer 56 Personal Travel Eco
9610 dissatisfied Male Loyal Customer 35 Personal Travel Eco
9620 satisfied Female Loyal Customer 61 Personal Travel Eco
9622 satisfied Female Loyal Customer 9 Personal Travel Eco
9623 satisfied Female Loyal Customer 61 Personal Travel Business
9647 dissatisfied Male Loyal Customer 61 Personal Travel Eco
9653 satisfied Female Loyal Customer 22 Personal Travel Eco
9670 satisfied Female Loyal Customer 25 Personal Travel Eco Plus
9700 satisfied Female Loyal Customer 8 Personal Travel Eco
9716 satisfied Female Loyal Customer 42 Personal Travel Eco Plus
9729 satisfied Female Loyal Customer 53 Personal Travel Eco Plus
9749 dissatisfied Male Loyal Customer 68 Personal Travel Eco
9754 dissatisfied Male Loyal Customer 59 Personal Travel Business
9777 satisfied Female Loyal Customer 21 Personal Travel Eco
9797 dissatisfied Male Loyal Customer 24 Personal Travel Eco
9829 dissatisfied Male Loyal Customer 47 Personal Travel Eco Plus
9865 dissatisfied Male Loyal Customer 40 Personal Travel Eco
9917 dissatisfied Male Loyal Customer 65 Personal Travel Business
9924 dissatisfied Male Loyal Customer 49 Personal Travel Eco
9933 satisfied Female Loyal Customer 34 Personal Travel Eco Plus
9937 dissatisfied Male Loyal Customer 35 Personal Travel Eco
9945 satisfied Female Loyal Customer 50 Personal Travel Eco
9948 satisfied Female Loyal Customer 26 Personal Travel Eco
9949 satisfied Female Loyal Customer 53 Personal Travel Eco Plus
9958 dissatisfied Male Loyal Customer 52 Personal Travel Eco
9970 satisfied Female Loyal Customer 8 Personal Travel Eco
9975 dissatisfied Male Loyal Customer 19 Personal Travel Eco
9986 satisfied Female Loyal Customer 38 Personal Travel Eco Plus
9993 satisfied Female Loyal Customer 66 Personal Travel Business
9995 dissatisfied Male Loyal Customer 29 Personal Travel Eco
10017 dissatisfied Male Loyal Customer 16 Personal Travel Eco
10021 dissatisfied Male Loyal Customer 53 Personal Travel Eco
10029 satisfied Female Loyal Customer 16 Personal Travel Eco
10037 dissatisfied Male Loyal Customer 52 Personal Travel Business
10044 satisfied Female Loyal Customer 64 Personal Travel Eco
10057 satisfied Female Loyal Customer 31 Personal Travel Eco Plus
10066 satisfied Female Loyal Customer 66 Personal Travel Eco
10085 satisfied Female Loyal Customer 25 Personal Travel Eco
10089 satisfied Female Loyal Customer 23 Personal Travel Business
10105 dissatisfied Male Loyal Customer 69 Personal Travel Business
10112 satisfied Female Loyal Customer 60 Personal Travel Eco
10148 satisfied Female Loyal Customer 16 Personal Travel Eco
10149 dissatisfied Male Loyal Customer 31 Personal Travel Eco Plus
10221 dissatisfied Male Loyal Customer 55 Personal Travel Eco
10233 satisfied Female Loyal Customer 57 Personal Travel Eco
10241 satisfied Female Loyal Customer 26 Personal Travel Eco Plus
10244 satisfied Female Loyal Customer 14 Personal Travel Eco
10289 dissatisfied Male Loyal Customer 43 Personal Travel Eco
10318 dissatisfied Male Loyal Customer 53 Personal Travel Eco
10329 dissatisfied Female Loyal Customer 11 Personal Travel Eco
10356 satisfied Female Loyal Customer 51 Personal Travel Eco
10387 satisfied Female Loyal Customer 56 Personal Travel Eco
10400 satisfied Female Loyal Customer 64 Personal Travel Eco
10407 dissatisfied Male Loyal Customer 40 Personal Travel Eco
10428 dissatisfied Male Loyal Customer 12 Personal Travel Eco Plus
10429 satisfied Female Loyal Customer 24 Personal Travel Eco
10431 dissatisfied Male Loyal Customer 30 Personal Travel Eco Plus
10435 satisfied Female Loyal Customer 22 Personal Travel Eco
10462 dissatisfied Male Loyal Customer 41 Personal Travel Business
10476 dissatisfied Male Loyal Customer 15 Personal Travel Eco Plus
10485 satisfied Female Loyal Customer 21 Personal Travel Business
10490 dissatisfied Male Loyal Customer 17 Personal Travel Business
10501 satisfied Female Loyal Customer 24 Personal Travel Eco
10515 satisfied Female Loyal Customer 15 Personal Travel Eco
10521 dissatisfied Male Loyal Customer 22 Personal Travel Eco
10538 satisfied Female Loyal Customer 21 Personal Travel Eco Plus
10565 dissatisfied Male Loyal Customer 20 Personal Travel Eco
10573 satisfied Female Loyal Customer 53 Personal Travel Eco
10590 satisfied Female Loyal Customer 15 Personal Travel Eco
10591 dissatisfied Male Loyal Customer 30 Personal Travel Eco
10605 satisfied Female Loyal Customer 62 Personal Travel Eco
10643 satisfied Female Loyal Customer 40 Personal Travel Eco
10646 satisfied Female Loyal Customer 31 Personal Travel Eco
10673 satisfied Female Loyal Customer 27 Personal Travel Business
10675 satisfied Female Loyal Customer 17 Personal Travel Eco Plus
10676 dissatisfied Male Loyal Customer 59 Personal Travel Eco
10683 satisfied Female Loyal Customer 29 Personal Travel Eco
10684 dissatisfied Male Loyal Customer 65 Personal Travel Eco
10736 dissatisfied Male Loyal Customer 35 Personal Travel Eco
10748 dissatisfied Male Loyal Customer 13 Personal Travel Eco
10766 dissatisfied Male Loyal Customer 30 Personal Travel Eco
10793 satisfied Female Loyal Customer 31 Personal Travel Business
10795 dissatisfied Male Loyal Customer 29 Personal Travel Eco
10816 dissatisfied Male Loyal Customer 21 Personal Travel Eco
10834 satisfied Female Loyal Customer 50 Personal Travel Eco
10847 dissatisfied Male Loyal Customer 13 Personal Travel Eco
10849 dissatisfied Male Loyal Customer 69 Personal Travel Eco
10857 dissatisfied Male Loyal Customer 23 Personal Travel Eco
10890 dissatisfied Male Loyal Customer 38 Personal Travel Eco
10908 dissatisfied Male Loyal Customer 37 Personal Travel Business
10909 satisfied Female Loyal Customer 31 Personal Travel Eco Plus
10919 dissatisfied Male Loyal Customer 39 Personal Travel Eco
10924 satisfied Female Loyal Customer 36 Personal Travel Eco
10933 satisfied Female Loyal Customer 63 Personal Travel Eco Plus
10937 satisfied Female Loyal Customer 50 Personal Travel Business
10942 dissatisfied Male Loyal Customer 54 Personal Travel Eco
10956 dissatisfied Male Loyal Customer 63 Personal Travel Eco
10982 dissatisfied Male Loyal Customer 41 Personal Travel Eco
10991 satisfied Female Loyal Customer 39 Personal Travel Eco Plus
10993 dissatisfied Male Loyal Customer 54 Personal Travel Eco
11005 dissatisfied Male Loyal Customer 66 Personal Travel Eco
11014 satisfied Female Loyal Customer 7 Personal Travel Eco
11108 dissatisfied Male Loyal Customer 28 Personal Travel Business
11130 satisfied Female Loyal Customer 31 Personal Travel Eco
11135 satisfied Female Loyal Customer 64 Personal Travel Eco
11139 satisfied Female Loyal Customer 22 Personal Travel Eco
11148 satisfied Female Loyal Customer 42 Personal Travel Eco
11152 dissatisfied Male Loyal Customer 38 Personal Travel Business
11156 satisfied Female Loyal Customer 7 Personal Travel Eco
11205 satisfied Female Loyal Customer 31 Personal Travel Eco
11208 dissatisfied Male Loyal Customer 19 Personal Travel Eco
11219 satisfied Female Loyal Customer 66 Personal Travel Eco
11230 dissatisfied Male Loyal Customer 24 Personal Travel Eco Plus
11234 dissatisfied Male Loyal Customer 26 Personal Travel Eco Plus
11263 satisfied Female Loyal Customer 15 Personal Travel Eco
11273 satisfied Female Loyal Customer 18 Personal Travel Eco
11282 dissatisfied Male Loyal Customer 21 Personal Travel Eco Plus
11288 satisfied Female Loyal Customer 36 Personal Travel Eco
11322 dissatisfied Male Loyal Customer 19 Personal Travel Eco
11325 satisfied Female Loyal Customer 13 Personal Travel Business
11331 satisfied Female Loyal Customer 68 Personal Travel Eco Plus
11350 satisfied Female Loyal Customer 40 Personal Travel Eco
11357 dissatisfied Male Loyal Customer 10 Personal Travel Eco
11360 dissatisfied Male Loyal Customer 7 Personal Travel Eco Plus
11387 satisfied Female Loyal Customer 12 Personal Travel Eco
11402 satisfied Female Loyal Customer 23 Personal Travel Eco
11416 dissatisfied Male Loyal Customer 17 Personal Travel Eco
11431 satisfied Female Loyal Customer 35 Personal Travel Eco Plus
11463 dissatisfied Male Loyal Customer 68 Personal Travel Eco
11464 satisfied Female Loyal Customer 22 Personal Travel Eco
11472 dissatisfied Male Loyal Customer 24 Personal Travel Eco
11475 dissatisfied Male Loyal Customer 42 Personal Travel Business
11501 satisfied Female Loyal Customer 32 Personal Travel Eco
11505 dissatisfied Male Loyal Customer 29 Personal Travel Eco
11510 dissatisfied Male Loyal Customer 51 Personal Travel Eco
11521 satisfied Female Loyal Customer 33 Personal Travel Eco
11523 dissatisfied Male Loyal Customer 29 Personal Travel Eco
11530 satisfied Female Loyal Customer 27 Personal Travel Eco
11564 dissatisfied Male Loyal Customer 37 Personal Travel Eco
11582 dissatisfied Male Loyal Customer 14 Personal Travel Eco
11603 satisfied Female Loyal Customer 23 Personal Travel Eco
11608 dissatisfied Male Loyal Customer 46 Personal Travel Eco
11650 satisfied Female Loyal Customer 49 Personal Travel Eco
11657 satisfied Female Loyal Customer 36 Personal Travel Eco
11659 dissatisfied Male Loyal Customer 21 Personal Travel Eco
11670 satisfied Female Loyal Customer 61 Personal Travel Eco
11679 dissatisfied Male Loyal Customer 46 Personal Travel Eco
11687 dissatisfied Male Loyal Customer 16 Personal Travel Eco
11704 satisfied Female Loyal Customer 12 Personal Travel Eco
11711 satisfied Female Loyal Customer 27 Personal Travel Eco
11714 dissatisfied Male Loyal Customer 69 Personal Travel Eco
11733 satisfied Female Loyal Customer 17 Personal Travel Eco Plus
11746 satisfied Female Loyal Customer 48 Personal Travel Eco
11752 satisfied Female Loyal Customer 40 Personal Travel Eco
11753 satisfied Female Loyal Customer 61 Personal Travel Eco
11756 satisfied Female Loyal Customer 20 Personal Travel Eco
11768 dissatisfied Male Loyal Customer 36 Personal Travel Eco
11823 satisfied Female Loyal Customer 58 Personal Travel Eco
11830 dissatisfied Male Loyal Customer 13 Personal Travel Eco
11839 dissatisfied Male Loyal Customer 70 Personal Travel Eco
11841 dissatisfied Male Loyal Customer 57 Personal Travel Eco
11848 satisfied Female Loyal Customer 12 Personal Travel Eco
11886 satisfied Female Loyal Customer 70 Personal Travel Eco
11913 satisfied Female Loyal Customer 35 Personal Travel Eco
11917 dissatisfied Male Loyal Customer 35 Personal Travel Eco
11924 satisfied Female Loyal Customer 24 Personal Travel Eco
11926 dissatisfied Male Loyal Customer 65 Personal Travel Eco
11938 satisfied Female Loyal Customer 68 Personal Travel Eco
11939 dissatisfied Male Loyal Customer 19 Personal Travel Eco
11963 satisfied Female Loyal Customer 49 Personal Travel Eco
11985 satisfied Female Loyal Customer 21 Personal Travel Eco
11994 dissatisfied Male Loyal Customer 30 Personal Travel Eco
12013 dissatisfied Male Loyal Customer 20 Personal Travel Eco
12016 satisfied Female Loyal Customer 39 Personal Travel Eco
12020 satisfied Female Loyal Customer 68 Personal Travel Eco
12053 satisfied Female Loyal Customer 33 Personal Travel Eco
12060 satisfied Female Loyal Customer 56 Personal Travel Eco Plus
12065 dissatisfied Male Loyal Customer 52 Personal Travel Eco
12067 dissatisfied Male Loyal Customer 10 Personal Travel Eco Plus
12076 satisfied Female Loyal Customer 38 Personal Travel Eco
12088 satisfied Female Loyal Customer 54 Personal Travel Eco
12089 satisfied Female Loyal Customer 43 Personal Travel Eco
12091 dissatisfied Male Loyal Customer 69 Personal Travel Eco
12093 dissatisfied Male Loyal Customer 42 Personal Travel Eco
12101 dissatisfied Male Loyal Customer 25 Personal Travel Eco
12130 dissatisfied Male Loyal Customer 67 Personal Travel Eco
12139 dissatisfied Male Loyal Customer 66 Personal Travel Eco
12146 satisfied Female Loyal Customer 60 Personal Travel Eco
12172 dissatisfied Male Loyal Customer 21 Personal Travel Eco
12268 satisfied Female Loyal Customer 20 Personal Travel Eco Plus
12281 dissatisfied Male Loyal Customer 9 Personal Travel Eco
12283 satisfied Female Loyal Customer 62 Personal Travel Eco
12298 dissatisfied Male Loyal Customer 39 Personal Travel Eco
12311 satisfied Female Loyal Customer 60 Personal Travel Eco
12316 dissatisfied Male Loyal Customer 53 Personal Travel Eco
12370 satisfied Female Loyal Customer 67 Personal Travel Eco
12372 satisfied Female Loyal Customer 60 Personal Travel Eco
12386 satisfied Female Loyal Customer 8 Personal Travel Eco
12403 dissatisfied Male Loyal Customer 17 Personal Travel Eco
12411 satisfied Female Loyal Customer 27 Personal Travel Eco
12415 satisfied Female Loyal Customer 40 Personal Travel Eco
12419 dissatisfied Male Loyal Customer 10 Personal Travel Business
12424 dissatisfied Male Loyal Customer 38 Personal Travel Eco
12426 dissatisfied Male Loyal Customer 24 Personal Travel Eco
12427 satisfied Female Loyal Customer 39 Personal Travel Eco
12430 satisfied Female Loyal Customer 46 Personal Travel Eco
12434 dissatisfied Male Loyal Customer 28 Personal Travel Eco
12452 dissatisfied Male Loyal Customer 21 Personal Travel Eco
12460 satisfied Female Loyal Customer 37 Personal Travel Eco
12470 dissatisfied Male Loyal Customer 62 Personal Travel Eco
12476 dissatisfied Male Loyal Customer 45 Personal Travel Eco
12481 satisfied Female Loyal Customer 63 Personal Travel Eco
12493 dissatisfied Male Loyal Customer 54 Personal Travel Eco
12509 dissatisfied Male Loyal Customer 51 Personal Travel Eco
12517 dissatisfied Male Loyal Customer 20 Personal Travel Eco Plus
12523 dissatisfied Male Loyal Customer 68 Personal Travel Eco
12532 satisfied Female Loyal Customer 17 Personal Travel Eco
12547 dissatisfied Male Loyal Customer 31 Personal Travel Eco
12556 dissatisfied Male Loyal Customer 30 Personal Travel Eco
12558 satisfied Female Loyal Customer 67 Personal Travel Eco
12559 satisfied Female Loyal Customer 64 Personal Travel Eco
12570 satisfied Female Loyal Customer 22 Personal Travel Eco
12590 satisfied Female Loyal Customer 14 Personal Travel Eco
12602 satisfied Female Loyal Customer 54 Personal Travel Eco
12606 satisfied Female Loyal Customer 32 Personal Travel Eco
12628 dissatisfied Male Loyal Customer 14 Personal Travel Eco
12640 satisfied Female Loyal Customer 42 Personal Travel Eco
12659 dissatisfied Female Loyal Customer 8 Personal Travel Eco
12669 satisfied Female Loyal Customer 19 Personal Travel Eco
12679 satisfied Female Loyal Customer 48 Personal Travel Eco
12685 dissatisfied Male Loyal Customer 26 Personal Travel Eco
12697 satisfied Female Loyal Customer 21 Personal Travel Eco
12702 dissatisfied Male Loyal Customer 34 Personal Travel Eco
12708 dissatisfied Male Loyal Customer 33 Personal Travel Eco
12718 satisfied Female Loyal Customer 58 Personal Travel Eco
12723 dissatisfied Male Loyal Customer 40 Personal Travel Eco
12743 dissatisfied Male Loyal Customer 61 Personal Travel Eco
12785 dissatisfied Male Loyal Customer 62 Personal Travel Eco
12813 satisfied Female Loyal Customer 47 Personal Travel Eco
12814 satisfied Female Loyal Customer 9 Personal Travel Eco
12819 satisfied Female Loyal Customer 56 Personal Travel Eco
12834 dissatisfied Male Loyal Customer 14 Personal Travel Eco
12842 satisfied Female Loyal Customer 12 Personal Travel Eco
12856 dissatisfied Male Loyal Customer 68 Personal Travel Eco
12868 dissatisfied Male Loyal Customer 65 Personal Travel Eco
12873 dissatisfied Male Loyal Customer 51 Personal Travel Eco Plus
12876 satisfied Female Loyal Customer 24 Personal Travel Eco
12893 satisfied Female Loyal Customer 67 Personal Travel Business
12898 satisfied Female Loyal Customer 53 Personal Travel Eco
12937 satisfied Female Loyal Customer 65 Personal Travel Eco
12950 satisfied Female Loyal Customer 48 Personal Travel Business
12951 satisfied Female Loyal Customer 62 Personal Travel Eco
12959 dissatisfied Male Loyal Customer 69 Personal Travel Eco
12995 satisfied Female Loyal Customer 61 Personal Travel Eco
12998 dissatisfied Male Loyal Customer 11 Personal Travel Eco
13015 satisfied Female Loyal Customer 54 Personal Travel Eco
13059 dissatisfied Male Loyal Customer 29 Personal Travel Eco
13067 satisfied Female Loyal Customer 33 Personal Travel Business
13073 dissatisfied Male Loyal Customer 28 Personal Travel Eco
13076 satisfied Female Loyal Customer 34 Personal Travel Eco
13092 satisfied Female Loyal Customer 65 Personal Travel Eco
13104 dissatisfied Male Loyal Customer 9 Personal Travel Eco Plus
13111 dissatisfied Male Loyal Customer 62 Personal Travel Eco
13113 dissatisfied Male Loyal Customer 55 Personal Travel Eco Plus
13115 dissatisfied Male Loyal Customer 45 Personal Travel Eco
13143 dissatisfied Male Loyal Customer 60 Personal Travel Eco
13152 satisfied Female Loyal Customer 26 Personal Travel Eco Plus
13153 dissatisfied Male Loyal Customer 39 Personal Travel Eco
13175 satisfied Female Loyal Customer 49 Personal Travel Eco
13191 dissatisfied Male Loyal Customer 13 Personal Travel Eco
13202 satisfied Female Loyal Customer 35 Personal Travel Eco Plus
13206 satisfied Female Loyal Customer 32 Personal Travel Eco
13212 satisfied Female Loyal Customer 38 Personal Travel Eco
13230 satisfied Female Loyal Customer 25 Personal Travel Eco
13253 satisfied Female Loyal Customer 69 Personal Travel Eco
13266 satisfied Female Loyal Customer 42 Personal Travel Eco
13271 dissatisfied Male Loyal Customer 35 Personal Travel Eco
13309 satisfied Female Loyal Customer 21 Personal Travel Eco
13354 dissatisfied Male Loyal Customer 46 Personal Travel Eco
13366 dissatisfied Male Loyal Customer 61 Personal Travel Eco
13367 dissatisfied Male Loyal Customer 53 Personal Travel Eco
13376 satisfied Female Loyal Customer 67 Personal Travel Eco Plus
13378 dissatisfied Male Loyal Customer 57 Personal Travel Eco Plus
13388 dissatisfied Female Loyal Customer 63 Personal Travel Eco
13401 dissatisfied Male Loyal Customer 61 Personal Travel Eco
13407 dissatisfied Male Loyal Customer 13 Personal Travel Eco Plus
13458 dissatisfied Male Loyal Customer 33 Personal Travel Eco
13459 dissatisfied Male Loyal Customer 10 Personal Travel Eco Plus
13467 dissatisfied Male Loyal Customer 24 Personal Travel Eco
13470 dissatisfied Male Loyal Customer 54 Personal Travel Eco
13543 satisfied Female Loyal Customer 67 Personal Travel Eco
13573 satisfied Female Loyal Customer 42 Personal Travel Eco
13592 satisfied Female Loyal Customer 60 Personal Travel Eco
13649 satisfied Female Loyal Customer 66 Personal Travel Eco
13698 satisfied Female Loyal Customer 39 Personal Travel Eco
13704 dissatisfied Male Loyal Customer 43 Personal Travel Business
13716 dissatisfied Male Loyal Customer 40 Personal Travel Eco
13750 dissatisfied Male Loyal Customer 68 Personal Travel Eco
13764 satisfied Female Loyal Customer 31 Personal Travel Business
13768 dissatisfied Male Loyal Customer 41 Personal Travel Eco
13819 satisfied Female Loyal Customer 14 Personal Travel Eco
13826 dissatisfied Male Loyal Customer 52 Personal Travel Business
13828 dissatisfied Female Loyal Customer 39 Personal Travel Eco
13841 dissatisfied Male Loyal Customer 30 Personal Travel Eco
13844 dissatisfied Male Loyal Customer 54 Personal Travel Eco Plus
13895 dissatisfied Male Loyal Customer 49 Personal Travel Eco
13915 dissatisfied Male Loyal Customer 43 Personal Travel Eco
13923 satisfied Female Loyal Customer 68 Personal Travel Eco
13947 dissatisfied Male Loyal Customer 51 Personal Travel Eco
13955 satisfied Female Loyal Customer 42 Personal Travel Eco
13956 dissatisfied Male Loyal Customer 68 Personal Travel Eco
13957 satisfied Female Loyal Customer 37 Personal Travel Eco
14006 satisfied Female Loyal Customer 46 Personal Travel Eco Plus
14012 dissatisfied Male Loyal Customer 70 Personal Travel Eco
14023 satisfied Female Loyal Customer 41 Personal Travel Eco
14026 dissatisfied Male Loyal Customer 54 Personal Travel Eco
14031 dissatisfied Male Loyal Customer 41 Personal Travel Eco
14067 dissatisfied Male Loyal Customer 14 Personal Travel Business
14072 dissatisfied Male Loyal Customer 45 Personal Travel Eco
14080 dissatisfied Male Loyal Customer 41 Personal Travel Eco
14094 dissatisfied Male Loyal Customer 22 Personal Travel Business
14111 dissatisfied Male Loyal Customer 24 Personal Travel Eco
14118 satisfied Female Loyal Customer 39 Personal Travel Eco
14130 dissatisfied Male Loyal Customer 12 Personal Travel Eco
14135 dissatisfied Male Loyal Customer 55 Personal Travel Eco
14138 dissatisfied Male Loyal Customer 58 Personal Travel Eco
14148 satisfied Female Loyal Customer 46 Personal Travel Eco
14154 satisfied Female Loyal Customer 65 Personal Travel Eco
14155 dissatisfied Male Loyal Customer 12 Personal Travel Eco
14157 satisfied Female Loyal Customer 64 Personal Travel Eco
14159 dissatisfied Male Loyal Customer 41 Personal Travel Eco Plus
14165 satisfied Female Loyal Customer 69 Personal Travel Eco
14174 satisfied Female Loyal Customer 25 Personal Travel Business
14186 dissatisfied Male Loyal Customer 37 Personal Travel Eco
14197 satisfied Female Loyal Customer 20 Personal Travel Eco
14201 dissatisfied Male Loyal Customer 52 Personal Travel Eco
14219 dissatisfied Male Loyal Customer 57 Personal Travel Eco
14270 satisfied Female Loyal Customer 67 Personal Travel Eco Plus
14278 satisfied Female Loyal Customer 31 Personal Travel Eco
14295 satisfied Female Loyal Customer 21 Personal Travel Eco Plus
14302 satisfied Female Loyal Customer 10 Personal Travel Eco Plus
14333 dissatisfied Male Loyal Customer 20 Personal Travel Eco
14350 satisfied Female Loyal Customer 11 Personal Travel Eco
14360 satisfied Female Loyal Customer 31 Personal Travel Eco
14374 satisfied Female Loyal Customer 20 Personal Travel Business
14389 dissatisfied Male Loyal Customer 31 Personal Travel Eco
14398 satisfied Female Loyal Customer 37 Personal Travel Eco
14411 satisfied Female Loyal Customer 49 Personal Travel Eco
14428 satisfied Female Loyal Customer 48 Personal Travel Eco Plus
14443 satisfied Female Loyal Customer 23 Personal Travel Eco
14445 satisfied Female Loyal Customer 64 Personal Travel Eco
14466 satisfied Female Loyal Customer 47 Personal Travel Eco
14484 satisfied Female Loyal Customer 12 Personal Travel Eco
14489 dissatisfied Male Loyal Customer 58 Personal Travel Eco
14526 dissatisfied Male Loyal Customer 59 Personal Travel Eco
14562 satisfied Female Loyal Customer 45 Personal Travel Eco
14567 satisfied Female Loyal Customer 32 Personal Travel Eco
14569 dissatisfied Male Loyal Customer 58 Personal Travel Eco
14574 dissatisfied Male Loyal Customer 39 Personal Travel Business
14584 satisfied Female Loyal Customer 70 Personal Travel Eco
14593 satisfied Female Loyal Customer 52 Personal Travel Eco
14599 dissatisfied Female Loyal Customer 13 Personal Travel Eco
14638 satisfied Female Loyal Customer 7 Personal Travel Eco
14647 dissatisfied Male Loyal Customer 35 Personal Travel Eco
14682 satisfied Female Loyal Customer 20 Personal Travel Eco
14699 dissatisfied Male Loyal Customer 16 Personal Travel Eco
14707 satisfied Female Loyal Customer 28 Personal Travel Eco
14722 dissatisfied Male Loyal Customer 60 Personal Travel Eco
14727 dissatisfied Male Loyal Customer 17 Personal Travel Eco
14737 satisfied Female Loyal Customer 19 Personal Travel Business
14740 satisfied Female Loyal Customer 45 Personal Travel Eco
14765 dissatisfied Male Loyal Customer 70 Personal Travel Eco
14768 dissatisfied Male Loyal Customer 12 Personal Travel Eco
14771 dissatisfied Male Loyal Customer 8 Personal Travel Eco
14794 dissatisfied Male Loyal Customer 68 Personal Travel Eco
14799 dissatisfied Male Loyal Customer 49 Personal Travel Eco
14805 satisfied Female Loyal Customer 27 Personal Travel Eco
14812 dissatisfied Male Loyal Customer 20 Personal Travel Eco Plus
14832 dissatisfied Male Loyal Customer 47 Personal Travel Eco
14856 dissatisfied Male Loyal Customer 56 Personal Travel Eco
14858 dissatisfied Male Loyal Customer 56 Personal Travel Eco
14859 dissatisfied Male Loyal Customer 32 Personal Travel Eco
14875 dissatisfied Male Loyal Customer 49 Personal Travel Eco
14879 satisfied Female Loyal Customer 22 Personal Travel Eco
14884 dissatisfied Male Loyal Customer 62 Personal Travel Eco
14891 satisfied Female Loyal Customer 59 Personal Travel Eco
14894 dissatisfied Male Loyal Customer 47 Personal Travel Eco
14898 satisfied Female Loyal Customer 19 Personal Travel Eco
14904 dissatisfied Female Loyal Customer 36 Personal Travel Eco
14920 satisfied Female Loyal Customer 59 Personal Travel Eco
14932 satisfied Female Loyal Customer 48 Personal Travel Eco Plus
14949 dissatisfied Male Loyal Customer 41 Personal Travel Eco
14973 dissatisfied Male Loyal Customer 31 Personal Travel Business
14974 dissatisfied Male Loyal Customer 16 Personal Travel Eco
14999 dissatisfied Male Loyal Customer 34 Personal Travel Eco
15023 dissatisfied Male Loyal Customer 8 Personal Travel Eco
15030 dissatisfied Male Loyal Customer 7 Personal Travel Eco
15039 satisfied Female Loyal Customer 16 Personal Travel Eco Plus
15042 dissatisfied Male Loyal Customer 7 Personal Travel Eco
15068 satisfied Female Loyal Customer 26 Personal Travel Eco
15070 dissatisfied Male Loyal Customer 38 Personal Travel Eco Plus
15074 dissatisfied Male Loyal Customer 64 Personal Travel Eco
15078 dissatisfied Male Loyal Customer 16 Personal Travel Eco Plus
15103 satisfied Female Loyal Customer 45 Personal Travel Eco
15127 satisfied Female Loyal Customer 13 Personal Travel Eco
15152 satisfied Female Loyal Customer 35 Personal Travel Eco
15178 dissatisfied Male Loyal Customer 13 Personal Travel Eco
15179 satisfied Female Loyal Customer 30 Personal Travel Eco
15203 dissatisfied Male Loyal Customer 61 Personal Travel Eco
15211 dissatisfied Male Loyal Customer 57 Personal Travel Eco
15226 satisfied Female Loyal Customer 14 Personal Travel Eco
15249 satisfied Female Loyal Customer 29 Personal Travel Eco
15265 satisfied Female Loyal Customer 11 Personal Travel Eco Plus
15268 dissatisfied Male Loyal Customer 16 Personal Travel Eco
15281 satisfied Female Loyal Customer 29 Personal Travel Eco
15302 dissatisfied Male Loyal Customer 47 Personal Travel Eco
15324 dissatisfied Male Loyal Customer 38 Personal Travel Eco
15350 satisfied Female Loyal Customer 50 Personal Travel Eco
15357 satisfied Female Loyal Customer 17 Personal Travel Eco
15391 satisfied Female Loyal Customer 33 Personal Travel Eco
15403 satisfied Female Loyal Customer 29 Personal Travel Eco
15410 dissatisfied Male Loyal Customer 19 Personal Travel Eco
15433 dissatisfied Male Loyal Customer 34 Personal Travel Eco
15440 satisfied Female Loyal Customer 24 Personal Travel Business
15442 dissatisfied Male Loyal Customer 23 Personal Travel Eco
15447 satisfied Female Loyal Customer 58 Personal Travel Eco
15448 dissatisfied Male Loyal Customer 37 Personal Travel Eco
15450 dissatisfied Male Loyal Customer 26 Personal Travel Business
15488 dissatisfied Male Loyal Customer 21 Personal Travel Eco Plus
15494 satisfied Female Loyal Customer 10 Personal Travel Eco Plus
15498 dissatisfied Female Loyal Customer 29 Personal Travel Eco
15519 satisfied Female Loyal Customer 42 Personal Travel Eco
15523 dissatisfied Male Loyal Customer 37 Personal Travel Eco
15525 dissatisfied Male Loyal Customer 27 Personal Travel Eco
15542 dissatisfied Male Loyal Customer 40 Personal Travel Eco
15550 dissatisfied Male Loyal Customer 14 Personal Travel Eco
15560 dissatisfied Male Loyal Customer 29 Personal Travel Eco
15565 satisfied Female Loyal Customer 31 Personal Travel Eco
15576 satisfied Female Loyal Customer 18 Personal Travel Eco
15626 dissatisfied Male Loyal Customer 33 Personal Travel Eco
15642 satisfied Female Loyal Customer 27 Personal Travel Eco
15666 dissatisfied Male Loyal Customer 15 Personal Travel Business
15672 satisfied Female Loyal Customer 48 Personal Travel Eco
15683 dissatisfied Male Loyal Customer 12 Personal Travel Eco
15686 satisfied Female Loyal Customer 8 Personal Travel Eco
15693 satisfied Female Loyal Customer 56 Personal Travel Eco
15751 dissatisfied Male Loyal Customer 59 Personal Travel Eco
15769 satisfied Female Loyal Customer 70 Personal Travel Eco
15800 satisfied Female Loyal Customer 69 Personal Travel Eco Plus
15801 dissatisfied Male Loyal Customer 28 Personal Travel Eco
15815 satisfied Female Loyal Customer 47 Personal Travel Eco
15841 satisfied Female Loyal Customer 51 Personal Travel Business
15897 dissatisfied Male Loyal Customer 63 Personal Travel Eco
15901 dissatisfied Female Loyal Customer 69 Personal Travel Eco Plus
15909 dissatisfied Male Loyal Customer 26 Personal Travel Eco
15918 dissatisfied Male Loyal Customer 30 Personal Travel Eco
15973 dissatisfied Male Loyal Customer 21 Personal Travel Eco
15982 dissatisfied Male Loyal Customer 12 Personal Travel Eco Plus
15996 satisfied Female Loyal Customer 22 Personal Travel Eco Plus
16008 dissatisfied Male Loyal Customer 61 Personal Travel Eco
16016 satisfied Female Loyal Customer 41 Personal Travel Eco
16028 satisfied Female Loyal Customer 47 Personal Travel Eco
16037 dissatisfied Male Loyal Customer 68 Personal Travel Eco
16044 dissatisfied Female Loyal Customer 26 Personal Travel Eco
16051 satisfied Female Loyal Customer 55 Personal Travel Eco
16055 satisfied Female Loyal Customer 33 Personal Travel Eco
16062 satisfied Female Loyal Customer 60 Personal Travel Eco
16068 satisfied Female Loyal Customer 16 Personal Travel Eco Plus
16082 satisfied Female Loyal Customer 36 Personal Travel Eco
16083 satisfied Female Loyal Customer 28 Personal Travel Eco
16090 dissatisfied Male Loyal Customer 8 Personal Travel Eco
16107 dissatisfied Male Loyal Customer 13 Personal Travel Eco
16113 satisfied Female Loyal Customer 64 Personal Travel Eco
16118 dissatisfied Male Loyal Customer 14 Personal Travel Eco
16122 dissatisfied Male Loyal Customer 35 Personal Travel Eco
16155 satisfied Female Loyal Customer 14 Personal Travel Eco Plus
16171 dissatisfied Male Loyal Customer 35 Personal Travel Business
16172 satisfied Female Loyal Customer 22 Personal Travel Eco
16194 dissatisfied Male Loyal Customer 39 Personal Travel Eco
16204 dissatisfied Male Loyal Customer 43 Personal Travel Eco
16214 satisfied Female Loyal Customer 21 Personal Travel Eco
16215 satisfied Female Loyal Customer 14 Personal Travel Eco Plus
16237 satisfied Female Loyal Customer 13 Personal Travel Eco
16240 dissatisfied Male Loyal Customer 38 Personal Travel Eco
16246 dissatisfied Male Loyal Customer 38 Personal Travel Eco
16265 satisfied Female Loyal Customer 30 Personal Travel Eco
16276 dissatisfied Male Loyal Customer 36 Personal Travel Eco Plus
16300 satisfied Female Loyal Customer 27 Personal Travel Eco Plus
16307 dissatisfied Male Loyal Customer 8 Personal Travel Eco
16315 dissatisfied Male Loyal Customer 27 Personal Travel Eco
16319 satisfied Female Loyal Customer 42 Personal Travel Eco
16330 satisfied Female Loyal Customer 59 Personal Travel Eco
16341 dissatisfied Male Loyal Customer 58 Personal Travel Business
16355 dissatisfied Male Loyal Customer 26 Personal Travel Eco
16368 dissatisfied Male Loyal Customer 56 Personal Travel Eco
16372 dissatisfied Male Loyal Customer 46 Personal Travel Eco
16416 dissatisfied Male Loyal Customer 14 Personal Travel Eco
16450 dissatisfied Male Loyal Customer 63 Personal Travel Eco
16464 dissatisfied Male Loyal Customer 66 Personal Travel Eco
16481 satisfied Female Loyal Customer 19 Personal Travel Eco
16483 satisfied Female Loyal Customer 15 Personal Travel Eco
16493 dissatisfied Male Loyal Customer 46 Personal Travel Eco
16500 satisfied Female Loyal Customer 51 Personal Travel Business
16510 dissatisfied Male Loyal Customer 34 Personal Travel Eco Plus
16525 satisfied Female Loyal Customer 19 Personal Travel Eco
16526 satisfied Female Loyal Customer 33 Personal Travel Eco
16549 dissatisfied Male Loyal Customer 31 Personal Travel Eco
16576 satisfied Female Loyal Customer 7 Personal Travel Eco
16594 satisfied Female Loyal Customer 15 Personal Travel Eco
16597 satisfied Female Loyal Customer 37 Personal Travel Eco
16619 satisfied Female Loyal Customer 17 Personal Travel Eco
16621 dissatisfied Male Loyal Customer 39 Personal Travel Eco
16625 satisfied Female Loyal Customer 70 Personal Travel Eco
16627 dissatisfied Male Loyal Customer 56 Personal Travel Eco
16655 dissatisfied Male Loyal Customer 32 Personal Travel Eco Plus
16672 satisfied Female Loyal Customer 26 Personal Travel Eco
16673 satisfied Female Loyal Customer 7 Personal Travel Eco
16681 satisfied Female Loyal Customer 33 Personal Travel Eco
16686 dissatisfied Male Loyal Customer 53 Personal Travel Eco
16712 dissatisfied Male Loyal Customer 49 Personal Travel Eco Plus
16724 dissatisfied Male Loyal Customer 31 Personal Travel Eco
16744 dissatisfied Male Loyal Customer 63 Personal Travel Eco
16751 dissatisfied Male Loyal Customer 7 Personal Travel Eco
16769 dissatisfied Male Loyal Customer 50 Personal Travel Eco
16788 dissatisfied Male Loyal Customer 23 Personal Travel Eco
16799 satisfied Female Loyal Customer 52 Personal Travel Eco
16805 dissatisfied Male Loyal Customer 52 Personal Travel Eco
16806 satisfied Female Loyal Customer 37 Personal Travel Eco
16810 dissatisfied Male Loyal Customer 17 Personal Travel Eco
16821 dissatisfied Male Loyal Customer 13 Personal Travel Eco
16832 dissatisfied Male Loyal Customer 62 Personal Travel Eco
16847 dissatisfied Male Loyal Customer 38 Personal Travel Eco Plus
16848 satisfied Female Loyal Customer 59 Personal Travel Eco
16850 dissatisfied Male Loyal Customer 59 Personal Travel Eco
16863 satisfied Female Loyal Customer 54 Personal Travel Eco
16902 satisfied Female Loyal Customer 13 Personal Travel Eco
16906 dissatisfied Male Loyal Customer 59 Personal Travel Eco Plus
16910 dissatisfied Male Loyal Customer 66 Personal Travel Eco
16912 dissatisfied Male Loyal Customer 48 Personal Travel Eco
16919 dissatisfied Male Loyal Customer 8 Personal Travel Eco
16921 satisfied Female Loyal Customer 65 Personal Travel Eco
16922 dissatisfied Male Loyal Customer 33 Personal Travel Eco
16979 dissatisfied Male Loyal Customer 10 Personal Travel Eco
16983 satisfied Female Loyal Customer 26 Personal Travel Eco Plus
16994 dissatisfied Male Loyal Customer 41 Personal Travel Eco
17022 dissatisfied Male Loyal Customer 8 Personal Travel Eco
17030 satisfied Female Loyal Customer 37 Personal Travel Eco
17032 satisfied Female Loyal Customer 27 Personal Travel Eco
17039 dissatisfied Male Loyal Customer 7 Personal Travel Eco Plus
17041 dissatisfied Male Loyal Customer 62 Personal Travel Eco
17060 satisfied Female Loyal Customer 16 Personal Travel Business
17075 dissatisfied Male Loyal Customer 31 Personal Travel Eco
17076 satisfied Female Loyal Customer 15 Personal Travel Eco
17078 dissatisfied Male Loyal Customer 41 Personal Travel Eco
17089 dissatisfied Male Loyal Customer 24 Personal Travel Eco
17105 satisfied Female Loyal Customer 60 Personal Travel Eco
17107 dissatisfied Male Loyal Customer 14 Personal Travel Eco
17116 dissatisfied Male Loyal Customer 27 Personal Travel Eco
17118 satisfied Female Loyal Customer 55 Personal Travel Eco
17121 satisfied Female Loyal Customer 22 Personal Travel Eco
17125 satisfied Female Loyal Customer 68 Personal Travel Eco
17134 satisfied Female Loyal Customer 17 Personal Travel Eco
17135 dissatisfied Male Loyal Customer 51 Personal Travel Eco
17151 dissatisfied Male Loyal Customer 38 Personal Travel Eco
17153 satisfied Female Loyal Customer 30 Personal Travel Eco
17164 satisfied Female Loyal Customer 69 Personal Travel Eco
17168 dissatisfied Male Loyal Customer 23 Personal Travel Eco
17206 dissatisfied Male Loyal Customer 67 Personal Travel Eco
17243 satisfied Female Loyal Customer 58 Personal Travel Eco
17264 satisfied Female Loyal Customer 49 Personal Travel Eco
17275 dissatisfied Female Loyal Customer 32 Personal Travel Eco
17287 dissatisfied Male Loyal Customer 50 Personal Travel Eco
17288 satisfied Female Loyal Customer 57 Personal Travel Eco
17294 dissatisfied Male Loyal Customer 67 Personal Travel Eco
17295 dissatisfied Male Loyal Customer 45 Personal Travel Eco
17302 satisfied Female Loyal Customer 52 Personal Travel Business
17311 satisfied Female Loyal Customer 17 Personal Travel Eco
17313 satisfied Female Loyal Customer 8 Personal Travel Eco
17339 satisfied Female Loyal Customer 31 Personal Travel Eco
17381 dissatisfied Male Loyal Customer 62 Personal Travel Eco
17393 satisfied Female Loyal Customer 67 Personal Travel Eco
17396 dissatisfied Male Loyal Customer 37 Personal Travel Eco
17419 satisfied Female Loyal Customer 35 Personal Travel Eco
17422 dissatisfied Male Loyal Customer 44 Personal Travel Eco
17429 dissatisfied Male Loyal Customer 68 Personal Travel Eco
17432 satisfied Female Loyal Customer 10 Personal Travel Eco
17433 satisfied Female Loyal Customer 32 Personal Travel Eco
17435 dissatisfied Male Loyal Customer 47 Personal Travel Eco
17447 satisfied Female Loyal Customer 53 Personal Travel Eco
17453 dissatisfied Male Loyal Customer 65 Personal Travel Eco
17484 dissatisfied Male Loyal Customer 69 Personal Travel Eco
17487 dissatisfied Male Loyal Customer 61 Personal Travel Eco
17500 dissatisfied Male Loyal Customer 19 Personal Travel Eco
17509 dissatisfied Male Loyal Customer 51 Personal Travel Eco
17540 dissatisfied Male Loyal Customer 29 Personal Travel Eco
17548 satisfied Female Loyal Customer 16 Personal Travel Business
17567 satisfied Female Loyal Customer 29 Personal Travel Eco
17594 dissatisfied Male Loyal Customer 67 Personal Travel Eco Plus
17607 satisfied Female Loyal Customer 56 Personal Travel Eco
17661 dissatisfied Male Loyal Customer 7 Personal Travel Eco
17665 dissatisfied Female Loyal Customer 67 Personal Travel Eco
17701 dissatisfied Male Loyal Customer 36 Personal Travel Eco
17711 satisfied Female Loyal Customer 29 Personal Travel Eco Plus
17713 dissatisfied Male Loyal Customer 18 Personal Travel Eco
17714 dissatisfied Male Loyal Customer 56 Personal Travel Eco
17717 dissatisfied Male Loyal Customer 21 Personal Travel Business
17730 satisfied Female Loyal Customer 68 Personal Travel Business
17733 satisfied Female Loyal Customer 31 Personal Travel Eco
17734 satisfied Female Loyal Customer 52 Personal Travel Eco
17752 dissatisfied Male Loyal Customer 50 Personal Travel Eco
17761 satisfied Female Loyal Customer 35 Personal Travel Eco
17767 dissatisfied Male Loyal Customer 49 Personal Travel Eco
17775 dissatisfied Male Loyal Customer 36 Personal Travel Eco
17788 satisfied Female Loyal Customer 48 Personal Travel Eco
17807 satisfied Female Loyal Customer 66 Personal Travel Eco
17810 dissatisfied Female Loyal Customer 26 Personal Travel Eco
17815 dissatisfied Male Loyal Customer 56 Personal Travel Eco Plus
17818 satisfied Female Loyal Customer 30 Personal Travel Eco Plus
17820 dissatisfied Male Loyal Customer 51 Personal Travel Eco
17875 satisfied Female Loyal Customer 47 Personal Travel Eco Plus
17878 satisfied Female Loyal Customer 65 Personal Travel Eco
17879 dissatisfied Male Loyal Customer 21 Personal Travel Eco
17889 dissatisfied Male Loyal Customer 24 Personal Travel Eco Plus
17893 satisfied Female Loyal Customer 51 Personal Travel Eco
17918 dissatisfied Male Loyal Customer 60 Personal Travel Eco
17921 satisfied Female Loyal Customer 67 Personal Travel Eco
17923 satisfied Female Loyal Customer 51 Personal Travel Eco
17946 satisfied Female Loyal Customer 44 Personal Travel Eco
17954 satisfied Female Loyal Customer 53 Personal Travel Eco
17956 dissatisfied Male Loyal Customer 58 Personal Travel Eco
17980 dissatisfied Male Loyal Customer 32 Personal Travel Eco
18001 satisfied Female Loyal Customer 32 Personal Travel Eco
18015 dissatisfied Male Loyal Customer 18 Personal Travel Eco
18024 satisfied Female Loyal Customer 23 Personal Travel Eco
18029 dissatisfied Male Loyal Customer 41 Personal Travel Eco
18036 satisfied Female Loyal Customer 58 Personal Travel Eco Plus
18065 dissatisfied Male Loyal Customer 65 Personal Travel Eco
18095 dissatisfied Male Loyal Customer 7 Personal Travel Eco
18140 dissatisfied Male Loyal Customer 30 Personal Travel Eco
18143 satisfied Female Loyal Customer 15 Personal Travel Eco Plus
18161 dissatisfied Male Loyal Customer 54 Personal Travel Eco
18165 satisfied Female Loyal Customer 14 Personal Travel Eco
18188 satisfied Female Loyal Customer 62 Personal Travel Eco Plus
18193 satisfied Female Loyal Customer 18 Personal Travel Eco Plus
18205 dissatisfied Male Loyal Customer 20 Personal Travel Eco Plus
18212 satisfied Female Loyal Customer 55 Personal Travel Eco
18223 dissatisfied Male Loyal Customer 22 Personal Travel Eco Plus
18225 dissatisfied Male Loyal Customer 65 Personal Travel Eco
18228 satisfied Female Loyal Customer 50 Personal Travel Eco
18233 satisfied Female Loyal Customer 27 Personal Travel Eco Plus
18263 satisfied Female Loyal Customer 50 Personal Travel Eco
18271 dissatisfied Male Loyal Customer 14 Personal Travel Eco
18307 satisfied Female Loyal Customer 18 Personal Travel Eco
18308 satisfied Female Loyal Customer 19 Personal Travel Eco
18325 dissatisfied Male Loyal Customer 11 Personal Travel Eco
18337 satisfied Female Loyal Customer 64 Personal Travel Eco
18342 dissatisfied Male Loyal Customer 18 Personal Travel Eco
18361 dissatisfied Male Loyal Customer 12 Personal Travel Eco
18371 satisfied Female Loyal Customer 47 Personal Travel Eco
18372 dissatisfied Male Loyal Customer 66 Personal Travel Eco
18444 dissatisfied Male Loyal Customer 21 Personal Travel Eco
18445 dissatisfied Male Loyal Customer 25 Personal Travel Eco
18472 dissatisfied Male Loyal Customer 28 Personal Travel Eco
18497 dissatisfied Male Loyal Customer 14 Personal Travel Eco
18511 satisfied Female Loyal Customer 23 Personal Travel Eco
18517 dissatisfied Male Loyal Customer 70 Personal Travel Business
18524 satisfied Female Loyal Customer 49 Personal Travel Eco
18538 dissatisfied Male Loyal Customer 50 Personal Travel Eco Plus
18549 dissatisfied Male Loyal Customer 37 Personal Travel Eco
18550 satisfied Female Loyal Customer 33 Personal Travel Eco
18560 satisfied Female Loyal Customer 68 Personal Travel Eco
18611 dissatisfied Male Loyal Customer 33 Personal Travel Eco
18616 dissatisfied Male Loyal Customer 11 Personal Travel Eco
18626 satisfied Female Loyal Customer 61 Personal Travel Eco Plus
18628 dissatisfied Male Loyal Customer 41 Personal Travel Eco
18629 dissatisfied Male Loyal Customer 67 Personal Travel Eco
18639 satisfied Female Loyal Customer 49 Personal Travel Eco Plus
18649 satisfied Female Loyal Customer 7 Personal Travel Eco
18658 satisfied Female Loyal Customer 24 Personal Travel Eco
18666 satisfied Female Loyal Customer 20 Personal Travel Eco
18690 dissatisfied Male Loyal Customer 63 Personal Travel Eco
18714 dissatisfied Male Loyal Customer 61 Personal Travel Eco
18720 satisfied Female Loyal Customer 31 Personal Travel Eco
18787 satisfied Female Loyal Customer 48 Personal Travel Business
18816 dissatisfied Male Loyal Customer 64 Personal Travel Eco
18817 dissatisfied Male Loyal Customer 31 Personal Travel Eco
18819 satisfied Female Loyal Customer 53 Personal Travel Business
18820 satisfied Female Loyal Customer 59 Personal Travel Eco
18836 satisfied Female Loyal Customer 39 Personal Travel Eco Plus
18837 satisfied Female Loyal Customer 69 Personal Travel Eco
18858 satisfied Female Loyal Customer 34 Personal Travel Eco
18863 dissatisfied Male Loyal Customer 70 Personal Travel Eco
18880 satisfied Female Loyal Customer 40 Personal Travel Eco
18888 dissatisfied Male Loyal Customer 26 Personal Travel Eco
18937 satisfied Female Loyal Customer 36 Personal Travel Business
18946 dissatisfied Male Loyal Customer 66 Personal Travel Eco
18952 dissatisfied Male Loyal Customer 61 Personal Travel Eco Plus
18981 dissatisfied Male Loyal Customer 41 Personal Travel Eco
18989 dissatisfied Male Loyal Customer 48 Personal Travel Eco
18998 satisfied Female Loyal Customer 44 Personal Travel Eco
19006 satisfied Female Loyal Customer 29 Personal Travel Eco
19019 satisfied Female Loyal Customer 40 Personal Travel Eco Plus
19021 satisfied Female Loyal Customer 41 Personal Travel Eco
19035 satisfied Female Loyal Customer 41 Personal Travel Eco
19066 satisfied Female Loyal Customer 64 Personal Travel Eco
19078 satisfied Female Loyal Customer 34 Personal Travel Eco
19092 dissatisfied Male Loyal Customer 12 Personal Travel Eco
19186 satisfied Female Loyal Customer 17 Personal Travel Eco
19188 satisfied Female Loyal Customer 52 Personal Travel Eco
19192 dissatisfied Male Loyal Customer 18 Personal Travel Eco
19196 dissatisfied Male Loyal Customer 29 Personal Travel Eco
19204 dissatisfied Male Loyal Customer 17 Personal Travel Eco
19208 satisfied Female Loyal Customer 55 Personal Travel Eco
19218 dissatisfied Male Loyal Customer 64 Personal Travel Eco Plus
19226 dissatisfied Male Loyal Customer 52 Personal Travel Business
19232 satisfied Female Loyal Customer 49 Personal Travel Eco
19239 dissatisfied Male Loyal Customer 22 Personal Travel Eco
19263 dissatisfied Male Loyal Customer 66 Personal Travel Eco
19268 satisfied Female Loyal Customer 53 Personal Travel Eco
19306 satisfied Female Loyal Customer 70 Personal Travel Eco
19314 satisfied Female Loyal Customer 38 Personal Travel Eco
19316 dissatisfied Male Loyal Customer 46 Personal Travel Eco
19333 satisfied Female Loyal Customer 22 Personal Travel Eco
19377 satisfied Female Loyal Customer 35 Personal Travel Eco
19389 dissatisfied Male Loyal Customer 53 Personal Travel Eco
19406 dissatisfied Male Loyal Customer 22 Personal Travel Eco
19441 satisfied Female Loyal Customer 12 Personal Travel Eco
19454 satisfied Female Loyal Customer 25 Personal Travel Eco
19457 dissatisfied Male Loyal Customer 41 Personal Travel Eco
19459 dissatisfied Male Loyal Customer 31 Personal Travel Eco
19476 satisfied Female Loyal Customer 37 Personal Travel Eco
19478 satisfied Female Loyal Customer 62 Personal Travel Eco
19479 dissatisfied Male Loyal Customer 15 Personal Travel Eco
19493 satisfied Female Loyal Customer 40 Personal Travel Eco
19509 satisfied Female Loyal Customer 64 Personal Travel Eco
19525 dissatisfied Male Loyal Customer 38 Personal Travel Eco
19526 satisfied Female Loyal Customer 38 Personal Travel Eco
19532 dissatisfied Male Loyal Customer 21 Personal Travel Business
19541 satisfied Female Loyal Customer 67 Personal Travel Eco
19544 dissatisfied Male Loyal Customer 25 Personal Travel Eco
19625 dissatisfied Male Loyal Customer 21 Personal Travel Eco Plus
19643 dissatisfied Male Loyal Customer 16 Personal Travel Eco
19647 dissatisfied Male Loyal Customer 26 Personal Travel Eco
19648 satisfied Female Loyal Customer 26 Personal Travel Eco
19649 satisfied Female Loyal Customer 33 Personal Travel Eco
19664 dissatisfied Male Loyal Customer 43 Personal Travel Eco
19671 satisfied Female Loyal Customer 62 Personal Travel Eco
19686 dissatisfied Male Loyal Customer 53 Personal Travel Business
19706 satisfied Female Loyal Customer 60 Personal Travel Eco
19742 dissatisfied Male Loyal Customer 50 Personal Travel Eco
19755 satisfied Female Loyal Customer 43 Personal Travel Eco
19774 satisfied Female Loyal Customer 39 Personal Travel Eco
19800 satisfied Female Loyal Customer 48 Personal Travel Eco
19816 dissatisfied Male Loyal Customer 8 Personal Travel Eco
19826 dissatisfied Male Loyal Customer 42 Personal Travel Eco Plus
19832 satisfied Female Loyal Customer 36 Personal Travel Eco
19834 satisfied Female Loyal Customer 65 Personal Travel Eco
19864 satisfied Female Loyal Customer 49 Personal Travel Eco
19867 dissatisfied Male Loyal Customer 61 Personal Travel Eco
19872 dissatisfied Male Loyal Customer 35 Personal Travel Eco
19889 dissatisfied Male Loyal Customer 26 Personal Travel Eco
19906 satisfied Female Loyal Customer 15 Personal Travel Eco
19914 satisfied Female Loyal Customer 65 Personal Travel Eco
19916 satisfied Female Loyal Customer 51 Personal Travel Business
19919 dissatisfied Male Loyal Customer 38 Personal Travel Eco
19929 satisfied Female Loyal Customer 8 Personal Travel Eco
19934 dissatisfied Male Loyal Customer 36 Personal Travel Eco
19952 dissatisfied Male Loyal Customer 18 Personal Travel Eco
19990 dissatisfied Male Loyal Customer 40 Personal Travel Eco
19992 satisfied Female Loyal Customer 17 Personal Travel Eco
19998 satisfied Female Loyal Customer 32 Personal Travel Eco
20024 dissatisfied Male Loyal Customer 45 Personal Travel Eco
20030 dissatisfied Male Loyal Customer 32 Personal Travel Eco
20049 dissatisfied Male Loyal Customer 23 Personal Travel Eco
20053 satisfied Female Loyal Customer 32 Personal Travel Eco
20055 satisfied Female Loyal Customer 59 Personal Travel Eco Plus
20062 dissatisfied Male Loyal Customer 15 Personal Travel Eco
20084 dissatisfied Male Loyal Customer 30 Personal Travel Eco
20107 dissatisfied Male Loyal Customer 30 Personal Travel Eco Plus
20144 satisfied Female Loyal Customer 10 Personal Travel Business
20147 dissatisfied Male Loyal Customer 54 Personal Travel Eco
20181 satisfied Female Loyal Customer 27 Personal Travel Eco
20218 satisfied Female Loyal Customer 47 Personal Travel Eco
20232 satisfied Female Loyal Customer 42 Personal Travel Eco
20250 dissatisfied Male Loyal Customer 68 Personal Travel Eco
20256 dissatisfied Male Loyal Customer 35 Personal Travel Eco
20277 satisfied Female Loyal Customer 29 Personal Travel Eco
20278 dissatisfied Male Loyal Customer 39 Personal Travel Eco
20291 dissatisfied Male Loyal Customer 49 Personal Travel Eco
20292 satisfied Female Loyal Customer 48 Personal Travel Eco
20325 satisfied Female Loyal Customer 16 Personal Travel Eco
20356 dissatisfied Male Loyal Customer 14 Personal Travel Eco
20391 satisfied Female Loyal Customer 64 Personal Travel Eco
20401 satisfied Female Loyal Customer 36 Personal Travel Eco
20403 satisfied Female Loyal Customer 15 Personal Travel Eco
20406 satisfied Female Loyal Customer 7 Personal Travel Eco Plus
20410 dissatisfied Male Loyal Customer 66 Personal Travel Eco
20426 dissatisfied Male Loyal Customer 12 Personal Travel Eco
20438 satisfied Female Loyal Customer 29 Personal Travel Eco
20460 satisfied Female Loyal Customer 36 Personal Travel Eco
20463 dissatisfied Male Loyal Customer 56 Personal Travel Eco
20518 satisfied Female Loyal Customer 55 Personal Travel Eco
20526 satisfied Female Loyal Customer 15 Personal Travel Eco
20543 dissatisfied Male Loyal Customer 30 Personal Travel Eco
20562 satisfied Female Loyal Customer 18 Personal Travel Eco
20576 satisfied Female Loyal Customer 53 Personal Travel Eco
20580 dissatisfied Male Loyal Customer 50 Personal Travel Eco
20596 satisfied Female Loyal Customer 59 Personal Travel Eco
20597 satisfied Female Loyal Customer 45 Personal Travel Eco
20604 dissatisfied Male Loyal Customer 42 Personal Travel Eco Plus
20605 satisfied Female Loyal Customer 48 Personal Travel Eco
20612 dissatisfied Male Loyal Customer 43 Personal Travel Eco
20615 satisfied Female Loyal Customer 17 Personal Travel Business
20626 satisfied Female Loyal Customer 36 Personal Travel Eco
20629 dissatisfied Male Loyal Customer 14 Personal Travel Eco
20630 dissatisfied Male Loyal Customer 7 Personal Travel Business
20634 dissatisfied Male Loyal Customer 38 Personal Travel Business
20677 satisfied Female Loyal Customer 13 Personal Travel Eco
20720 dissatisfied Male Loyal Customer 69 Personal Travel Eco
20740 satisfied Female Loyal Customer 39 Personal Travel Eco
20764 dissatisfied Male Loyal Customer 63 Personal Travel Business
20779 dissatisfied Male Loyal Customer 51 Personal Travel Eco
20786 satisfied Female Loyal Customer 19 Personal Travel Eco
20797 dissatisfied Male Loyal Customer 38 Personal Travel Business
20799 satisfied Female Loyal Customer 47 Personal Travel Business
20822 satisfied Female Loyal Customer 51 Personal Travel Eco
20827 satisfied Female Loyal Customer 44 Personal Travel Eco
20833 satisfied Female Loyal Customer 22 Personal Travel Eco Plus
20834 dissatisfied Male Loyal Customer 35 Personal Travel Eco
20837 dissatisfied Male Loyal Customer 19 Personal Travel Eco
20870 dissatisfied Male Loyal Customer 42 Personal Travel Eco
20895 satisfied Female Loyal Customer 15 Personal Travel Eco
20904 satisfied Female Loyal Customer 69 Personal Travel Business
20915 satisfied Female Loyal Customer 51 Personal Travel Eco
20919 satisfied Female Loyal Customer 16 Personal Travel Eco
20920 dissatisfied Male Loyal Customer 69 Personal Travel Eco
20924 dissatisfied Male Loyal Customer 49 Personal Travel Eco
20945 satisfied Female Loyal Customer 48 Personal Travel Eco Plus
20980 dissatisfied Male Loyal Customer 34 Personal Travel Eco
20985 satisfied Female Loyal Customer 37 Personal Travel Eco
20994 dissatisfied Male Loyal Customer 34 Personal Travel Eco
20999 satisfied Female Loyal Customer 58 Personal Travel Eco
21011 satisfied Female Loyal Customer 40 Personal Travel Eco
21034 dissatisfied Male Loyal Customer 56 Personal Travel Eco
21036 satisfied Female Loyal Customer 57 Personal Travel Eco Plus
21040 satisfied Female Loyal Customer 27 Personal Travel Eco
21058 dissatisfied Male Loyal Customer 29 Personal Travel Eco
21061 satisfied Female Loyal Customer 58 Personal Travel Business
21077 dissatisfied Male Loyal Customer 28 Personal Travel Eco
21100 satisfied Female Loyal Customer 53 Personal Travel Eco
21105 satisfied Female Loyal Customer 29 Personal Travel Eco
21109 satisfied Female Loyal Customer 67 Personal Travel Eco
21117 dissatisfied Male Loyal Customer 54 Personal Travel Eco
21118 dissatisfied Male Loyal Customer 22 Personal Travel Eco
21119 dissatisfied Male Loyal Customer 61 Personal Travel Business
21122 satisfied Female Loyal Customer 30 Personal Travel Eco
21123 satisfied Female Loyal Customer 26 Personal Travel Eco
21131 satisfied Female Loyal Customer 26 Personal Travel Eco
21163 dissatisfied Male Loyal Customer 23 Personal Travel Eco
21166 satisfied Female Loyal Customer 26 Personal Travel Eco
21191 dissatisfied Male Loyal Customer 53 Personal Travel Eco
21192 dissatisfied Male Loyal Customer 19 Personal Travel Business
21199 dissatisfied Male Loyal Customer 15 Personal Travel Eco
21221 dissatisfied Male Loyal Customer 44 Personal Travel Eco
21241 satisfied Female Loyal Customer 28 Personal Travel Eco
21253 dissatisfied Male Loyal Customer 30 Personal Travel Eco
21270 dissatisfied Male Loyal Customer 61 Personal Travel Eco
21283 dissatisfied Male Loyal Customer 42 Personal Travel Eco
21308 dissatisfied Male Loyal Customer 49 Personal Travel Eco Plus
21322 satisfied Female Loyal Customer 67 Personal Travel Eco
21334 dissatisfied Male Loyal Customer 29 Personal Travel Eco
21342 dissatisfied Male Loyal Customer 35 Personal Travel Eco
21363 satisfied Female Loyal Customer 70 Personal Travel Eco
21370 satisfied Female Loyal Customer 45 Personal Travel Eco
21386 satisfied Female Loyal Customer 70 Personal Travel Eco
21399 dissatisfied Male Loyal Customer 9 Personal Travel Eco
21412 dissatisfied Male Loyal Customer 42 Personal Travel Eco
21428 dissatisfied Male Loyal Customer 64 Personal Travel Eco
21463 satisfied Female Loyal Customer 40 Personal Travel Eco
21503 dissatisfied Male Loyal Customer 28 Personal Travel Business
21517 dissatisfied Male Loyal Customer 58 Personal Travel Eco
21529 satisfied Female Loyal Customer 62 Personal Travel Eco Plus
21538 satisfied Female Loyal Customer 27 Personal Travel Eco
21564 satisfied Female Loyal Customer 28 Personal Travel Eco Plus
21577 satisfied Female Loyal Customer 66 Personal Travel Eco
21585 dissatisfied Male Loyal Customer 25 Personal Travel Eco
21620 satisfied Female Loyal Customer 38 Personal Travel Eco
21634 dissatisfied Male Loyal Customer 59 Personal Travel Eco
21646 dissatisfied Male Loyal Customer 57 Personal Travel Eco
21654 dissatisfied Male Loyal Customer 9 Personal Travel Eco
21656 satisfied Female Loyal Customer 18 Personal Travel Eco
21667 dissatisfied Male Loyal Customer 26 Personal Travel Eco
21669 satisfied Female Loyal Customer 68 Personal Travel Eco Plus
21682 satisfied Female Loyal Customer 18 Personal Travel Eco
21717 dissatisfied Male Loyal Customer 38 Personal Travel Eco
21726 satisfied Female Loyal Customer 47 Personal Travel Eco
21768 dissatisfied Male Loyal Customer 9 Personal Travel Eco
21782 satisfied Female Loyal Customer 9 Personal Travel Eco
21787 satisfied Female Loyal Customer 23 Personal Travel Business
21792 satisfied Female Loyal Customer 47 Personal Travel Eco
21821 dissatisfied Male Loyal Customer 46 Personal Travel Eco
21844 dissatisfied Male Loyal Customer 31 Personal Travel Eco Plus
21865 satisfied Female Loyal Customer 18 Personal Travel Eco
21866 satisfied Female Loyal Customer 18 Personal Travel Eco
21868 dissatisfied Male Loyal Customer 32 Personal Travel Business
21870 dissatisfied Male Loyal Customer 61 Personal Travel Eco
21911 dissatisfied Male Loyal Customer 33 Personal Travel Eco
21921 dissatisfied Male Loyal Customer 70 Personal Travel Eco
21927 dissatisfied Male Loyal Customer 20 Personal Travel Eco
21935 dissatisfied Male Loyal Customer 40 Personal Travel Eco
21939 dissatisfied Female Loyal Customer 65 Personal Travel Eco
21942 dissatisfied Male Loyal Customer 63 Personal Travel Eco
21962 dissatisfied Male Loyal Customer 61 Personal Travel Eco Plus
21994 satisfied Female Loyal Customer 8 Personal Travel Eco
22016 dissatisfied Male Loyal Customer 55 Personal Travel Eco
22053 dissatisfied Male Loyal Customer 18 Personal Travel Eco
22087 satisfied Female Loyal Customer 48 Personal Travel Eco
22092 dissatisfied Male Loyal Customer 22 Personal Travel Eco
22099 satisfied Female Loyal Customer 42 Personal Travel Eco Plus
22111 satisfied Female Loyal Customer 69 Personal Travel Eco Plus
22134 satisfied Female Loyal Customer 43 Personal Travel Eco
22154 dissatisfied Male Loyal Customer 61 Personal Travel Eco
22169 satisfied Female Loyal Customer 37 Personal Travel Eco
22179 dissatisfied Male Loyal Customer 61 Personal Travel Eco
22211 dissatisfied Male Loyal Customer 55 Personal Travel Eco
22229 satisfied Female Loyal Customer 25 Personal Travel Eco
22240 satisfied Female Loyal Customer 55 Personal Travel Eco Plus
22250 satisfied Female Loyal Customer 49 Personal Travel Eco
22253 dissatisfied Male Loyal Customer 54 Personal Travel Eco Plus
22255 dissatisfied Male Loyal Customer 54 Personal Travel Eco
22294 dissatisfied Male Loyal Customer 58 Personal Travel Eco
22303 dissatisfied Male Loyal Customer 29 Personal Travel Eco
22333 satisfied Female Loyal Customer 29 Personal Travel Eco
22339 satisfied Female Loyal Customer 24 Personal Travel Eco
22340 dissatisfied Female Loyal Customer 50 Personal Travel Eco
22346 satisfied Female Loyal Customer 69 Personal Travel Eco
22354 satisfied Female Loyal Customer 67 Personal Travel Eco
22378 satisfied Female Loyal Customer 35 Personal Travel Eco
22385 dissatisfied Male Loyal Customer 52 Personal Travel Eco
22391 satisfied Female Loyal Customer 16 Personal Travel Eco Plus
22400 satisfied Female Loyal Customer 10 Personal Travel Business
22421 satisfied Female Loyal Customer 69 Personal Travel Eco
22433 dissatisfied Male Loyal Customer 51 Personal Travel Eco
22435 satisfied Female Loyal Customer 63 Personal Travel Eco
22443 dissatisfied Male Loyal Customer 67 Personal Travel Eco
22444 dissatisfied Male Loyal Customer 54 Personal Travel Eco
22446 satisfied Female Loyal Customer 58 Personal Travel Eco
22460 satisfied Female Loyal Customer 11 Personal Travel Eco
22464 dissatisfied Male Loyal Customer 8 Personal Travel Eco
22508 satisfied Female Loyal Customer 48 Personal Travel Eco
22511 satisfied Female Loyal Customer 9 Personal Travel Eco Plus
22515 dissatisfied Male Loyal Customer 48 Personal Travel Eco
22538 dissatisfied Male Loyal Customer 14 Personal Travel Eco
22550 satisfied Female Loyal Customer 68 Personal Travel Eco
22564 satisfied Female Loyal Customer 36 Personal Travel Eco
22568 dissatisfied Male Loyal Customer 22 Personal Travel Business
22570 satisfied Female Loyal Customer 7 Personal Travel Eco
22615 dissatisfied Male Loyal Customer 23 Personal Travel Eco Plus
22631 satisfied Female Loyal Customer 68 Personal Travel Eco Plus
22634 satisfied Female Loyal Customer 20 Personal Travel Eco
22640 dissatisfied Male Loyal Customer 15 Personal Travel Eco
22672 dissatisfied Male Loyal Customer 13 Personal Travel Eco Plus
22678 satisfied Female Loyal Customer 46 Personal Travel Eco Plus
22687 satisfied Female Loyal Customer 48 Personal Travel Eco
22689 satisfied Female Loyal Customer 46 Personal Travel Eco
22717 satisfied Female Loyal Customer 47 Personal Travel Eco
22735 satisfied Female Loyal Customer 67 Personal Travel Eco
22740 satisfied Female Loyal Customer 7 Personal Travel Eco
22744 satisfied Female Loyal Customer 32 Personal Travel Eco
22753 satisfied Female Loyal Customer 66 Personal Travel Eco Plus
22755 satisfied Female Loyal Customer 11 Personal Travel Eco
22759 dissatisfied Male Loyal Customer 26 Personal Travel Eco
22761 satisfied Female Loyal Customer 43 Personal Travel Eco
22769 dissatisfied Male Loyal Customer 36 Personal Travel Eco
22779 satisfied Female Loyal Customer 46 Personal Travel Eco
22789 dissatisfied Male Loyal Customer 46 Personal Travel Eco
22801 dissatisfied Male Loyal Customer 25 Personal Travel Eco
22814 dissatisfied Male Loyal Customer 28 Personal Travel Eco
22816 satisfied Female Loyal Customer 18 Personal Travel Eco
22821 dissatisfied Male Loyal Customer 55 Personal Travel Eco
22823 dissatisfied Male Loyal Customer 52 Personal Travel Eco Plus
22827 dissatisfied Male Loyal Customer 12 Personal Travel Eco
22846 satisfied Female Loyal Customer 42 Personal Travel Eco
22870 satisfied Female Loyal Customer 62 Personal Travel Eco
22915 dissatisfied Male Loyal Customer 22 Personal Travel Eco
22919 satisfied Female Loyal Customer 53 Personal Travel Eco
22937 satisfied Female Loyal Customer 43 Personal Travel Eco
22942 satisfied Female Loyal Customer 46 Personal Travel Eco
22944 dissatisfied Male Loyal Customer 32 Personal Travel Eco
22959 dissatisfied Male Loyal Customer 29 Personal Travel Business
22964 dissatisfied Male Loyal Customer 9 Personal Travel Eco Plus
22969 dissatisfied Male Loyal Customer 22 Personal Travel Eco
22971 dissatisfied Male Loyal Customer 48 Personal Travel Eco
22993 dissatisfied Male Loyal Customer 21 Personal Travel Eco
22995 dissatisfied Male Loyal Customer 30 Personal Travel Eco Plus
23012 dissatisfied Male Loyal Customer 46 Personal Travel Business
23014 satisfied Female Loyal Customer 47 Personal Travel Eco
23018 dissatisfied Male Loyal Customer 30 Personal Travel Eco
23037 satisfied Female Loyal Customer 31 Personal Travel Eco
23043 dissatisfied Male Loyal Customer 42 Personal Travel Eco
23073 satisfied Female Loyal Customer 57 Personal Travel Eco
23083 dissatisfied Male Loyal Customer 62 Personal Travel Business
23109 dissatisfied Male Loyal Customer 10 Personal Travel Eco
23111 dissatisfied Male Loyal Customer 36 Personal Travel Eco
23118 satisfied Female Loyal Customer 19 Personal Travel Eco
23130 satisfied Female Loyal Customer 21 Personal Travel Eco
23137 dissatisfied Male Loyal Customer 31 Personal Travel Business
23139 satisfied Female Loyal Customer 28 Personal Travel Eco
23140 dissatisfied Male Loyal Customer 13 Personal Travel Eco
23145 dissatisfied Male Loyal Customer 65 Personal Travel Eco
23151 satisfied Female Loyal Customer 29 Personal Travel Eco
23153 dissatisfied Male Loyal Customer 68 Personal Travel Eco
23184 satisfied Female Loyal Customer 69 Personal Travel Eco
23186 dissatisfied Male Loyal Customer 47 Personal Travel Eco
23199 dissatisfied Male Loyal Customer 9 Personal Travel Eco
23213 satisfied Female Loyal Customer 11 Personal Travel Eco
23225 satisfied Female Loyal Customer 8 Personal Travel Eco
23235 dissatisfied Male Loyal Customer 24 Personal Travel Eco
23246 dissatisfied Male Loyal Customer 13 Personal Travel Eco
23256 dissatisfied Male Loyal Customer 21 Personal Travel Eco
23257 dissatisfied Male Loyal Customer 53 Personal Travel Eco
23312 dissatisfied Male Loyal Customer 8 Personal Travel Eco
23320 dissatisfied Male Loyal Customer 22 Personal Travel Eco
23353 satisfied Female Loyal Customer 8 Personal Travel Eco
23390 satisfied Female Loyal Customer 30 Personal Travel Eco
23404 dissatisfied Male Loyal Customer 35 Personal Travel Eco
23422 dissatisfied Male Loyal Customer 9 Personal Travel Eco
23423 dissatisfied Male Loyal Customer 47 Personal Travel Eco Plus
23434 satisfied Female Loyal Customer 59 Personal Travel Eco Plus
23456 dissatisfied Male Loyal Customer 25 Personal Travel Business
23467 dissatisfied Male Loyal Customer 36 Personal Travel Eco
23474 dissatisfied Male Loyal Customer 49 Personal Travel Eco Plus
23488 dissatisfied Male Loyal Customer 40 Personal Travel Eco
23503 satisfied Female Loyal Customer 45 Personal Travel Eco
23553 satisfied Female Loyal Customer 33 Personal Travel Eco
23556 dissatisfied Male Loyal Customer 54 Personal Travel Eco
23563 satisfied Female Loyal Customer 63 Personal Travel Eco
23567 satisfied Female Loyal Customer 14 Personal Travel Eco Plus
23568 dissatisfied Male Loyal Customer 65 Personal Travel Eco
23573 dissatisfied Male Loyal Customer 24 Personal Travel Eco
23581 satisfied Female Loyal Customer 53 Personal Travel Eco
23588 dissatisfied Male Loyal Customer 27 Personal Travel Eco Plus
23634 satisfied Female Loyal Customer 69 Personal Travel Eco
23669 satisfied Female Loyal Customer 18 Personal Travel Eco
23683 satisfied Female Loyal Customer 62 Personal Travel Eco
23694 satisfied Female Loyal Customer 38 Personal Travel Eco
23700 satisfied Female Loyal Customer 41 Personal Travel Eco
23716 satisfied Female Loyal Customer 60 Personal Travel Eco
23726 satisfied Female Loyal Customer 51 Personal Travel Business
23747 satisfied Female Loyal Customer 69 Personal Travel Eco Plus
23758 satisfied Female Loyal Customer 32 Personal Travel Eco
23760 dissatisfied Male Loyal Customer 60 Personal Travel Eco
23780 dissatisfied Male Loyal Customer 43 Personal Travel Eco
23787 satisfied Female Loyal Customer 9 Personal Travel Eco
23788 satisfied Female Loyal Customer 39 Personal Travel Eco
23789 dissatisfied Male Loyal Customer 22 Personal Travel Eco
23799 dissatisfied Male Loyal Customer 23 Personal Travel Eco
23834 dissatisfied Male Loyal Customer 48 Personal Travel Business
23849 dissatisfied Male Loyal Customer 56 Personal Travel Eco
23855 dissatisfied Male Loyal Customer 37 Personal Travel Eco
23864 dissatisfied Male Loyal Customer 47 Personal Travel Business
23867 satisfied Female Loyal Customer 62 Personal Travel Eco
23899 dissatisfied Male Loyal Customer 26 Personal Travel Eco
23901 dissatisfied Male Loyal Customer 44 Personal Travel Eco
23909 dissatisfied Male Loyal Customer 50 Personal Travel Eco
23922 dissatisfied Male Loyal Customer 22 Personal Travel Eco
23957 satisfied Female Loyal Customer 50 Personal Travel Eco
23972 satisfied Female Loyal Customer 48 Personal Travel Eco
23974 dissatisfied Male Loyal Customer 38 Personal Travel Eco
24029 dissatisfied Male Loyal Customer 20 Personal Travel Eco
24056 satisfied Female Loyal Customer 22 Personal Travel Eco Plus
24076 satisfied Female Loyal Customer 52 Personal Travel Eco Plus
24078 dissatisfied Male Loyal Customer 28 Personal Travel Eco
24148 satisfied Female Loyal Customer 68 Personal Travel Eco Plus
24202 satisfied Female Loyal Customer 30 Personal Travel Eco
24206 satisfied Female Loyal Customer 68 Personal Travel Eco
24231 satisfied Female Loyal Customer 68 Personal Travel Eco
24248 satisfied Female Loyal Customer 26 Personal Travel Eco
24255 dissatisfied Male Loyal Customer 66 Personal Travel Eco
24289 dissatisfied Male Loyal Customer 60 Personal Travel Eco
24293 satisfied Female Loyal Customer 7 Personal Travel Eco
24300 dissatisfied Male Loyal Customer 10 Personal Travel Eco
24309 dissatisfied Female Loyal Customer 52 Personal Travel Eco
24312 dissatisfied Male Loyal Customer 45 Personal Travel Eco
24314 satisfied Female Loyal Customer 28 Personal Travel Eco Plus
24327 dissatisfied Male Loyal Customer 39 Personal Travel Eco
24340 dissatisfied Male Loyal Customer 24 Personal Travel Eco Plus
24348 dissatisfied Male Loyal Customer 15 Personal Travel Eco
24369 dissatisfied Male Loyal Customer 17 Personal Travel Eco
24380 satisfied Female Loyal Customer 37 Personal Travel Eco
24387 satisfied Female Loyal Customer 7 Personal Travel Eco
24398 satisfied Female Loyal Customer 26 Personal Travel Eco
24400 satisfied Female Loyal Customer 48 Personal Travel Business
24401 dissatisfied Male Loyal Customer 32 Personal Travel Eco
24422 satisfied Female Loyal Customer 18 Personal Travel Eco
24426 dissatisfied Male Loyal Customer 70 Personal Travel Eco
24432 dissatisfied Male Loyal Customer 37 Personal Travel Eco
24441 satisfied Female Loyal Customer 32 Personal Travel Eco
24442 dissatisfied Male Loyal Customer 69 Personal Travel Eco
24446 satisfied Female Loyal Customer 10 Personal Travel Eco
24474 dissatisfied Male Loyal Customer 52 Personal Travel Business
24482 dissatisfied Male Loyal Customer 27 Personal Travel Eco
24493 dissatisfied Male Loyal Customer 7 Personal Travel Eco
24495 satisfied Female Loyal Customer 53 Personal Travel Eco
24505 satisfied Female Loyal Customer 27 Personal Travel Eco
24517 dissatisfied Male Loyal Customer 49 Personal Travel Eco
24519 dissatisfied Male Loyal Customer 63 Personal Travel Eco
24535 satisfied Female Loyal Customer 59 Personal Travel Eco
24536 satisfied Female Loyal Customer 24 Personal Travel Eco
24549 satisfied Female Loyal Customer 27 Personal Travel Eco
24579 dissatisfied Male Loyal Customer 34 Personal Travel Eco
24592 dissatisfied Male Loyal Customer 44 Personal Travel Eco
24602 satisfied Female Loyal Customer 9 Personal Travel Eco
24616 satisfied Female Loyal Customer 65 Personal Travel Eco
24626 satisfied Female Loyal Customer 39 Personal Travel Eco
24657 satisfied Female Loyal Customer 8 Personal Travel Eco
24676 dissatisfied Male Loyal Customer 25 Personal Travel Eco
24684 satisfied Female Loyal Customer 68 Personal Travel Eco
24686 satisfied Female Loyal Customer 35 Personal Travel Eco
24690 dissatisfied Male Loyal Customer 51 Personal Travel Eco Plus
24697 satisfied Female Loyal Customer 67 Personal Travel Eco
24699 dissatisfied Male Loyal Customer 64 Personal Travel Eco
24715 satisfied Female Loyal Customer 32 Personal Travel Eco
24718 dissatisfied Male Loyal Customer 24 Personal Travel Eco
24757 dissatisfied Male Loyal Customer 45 Personal Travel Eco
24775 dissatisfied Male Loyal Customer 45 Personal Travel Eco
24789 satisfied Female Loyal Customer 65 Personal Travel Eco Plus
24792 dissatisfied Male Loyal Customer 69 Personal Travel Eco
24814 dissatisfied Male Loyal Customer 47 Personal Travel Business
24819 satisfied Female Loyal Customer 52 Personal Travel Eco Plus
24829 satisfied Female Loyal Customer 45 Personal Travel Eco Plus
24846 satisfied Female Loyal Customer 68 Personal Travel Eco
24855 dissatisfied Male Loyal Customer 33 Personal Travel Eco
24858 dissatisfied Male Loyal Customer 52 Personal Travel Eco
24861 satisfied Female Loyal Customer 44 Personal Travel Eco
24867 satisfied Female Loyal Customer 13 Personal Travel Eco
24869 dissatisfied Male Loyal Customer 33 Personal Travel Eco
24893 dissatisfied Male Loyal Customer 59 Personal Travel Eco
24919 satisfied Female Loyal Customer 44 Personal Travel Eco
24924 satisfied Female Loyal Customer 13 Personal Travel Eco
24927 satisfied Female Loyal Customer 19 Personal Travel Eco
24929 dissatisfied Male Loyal Customer 68 Personal Travel Eco
24940 satisfied Female Loyal Customer 30 Personal Travel Eco
24954 satisfied Female Loyal Customer 69 Personal Travel Eco
24971 satisfied Female Loyal Customer 50 Personal Travel Eco
24972 satisfied Female Loyal Customer 36 Personal Travel Eco
24987 satisfied Female Loyal Customer 43 Personal Travel Eco
25002 satisfied Female Loyal Customer 20 Personal Travel Business
25026 dissatisfied Male Loyal Customer 19 Personal Travel Eco
25028 dissatisfied Male Loyal Customer 12 Personal Travel Business
25034 dissatisfied Male Loyal Customer 48 Personal Travel Eco
25047 satisfied Female Loyal Customer 24 Personal Travel Eco
25053 dissatisfied Male Loyal Customer 44 Personal Travel Eco
25059 satisfied Female Loyal Customer 7 Personal Travel Eco
25074 dissatisfied Male Loyal Customer 38 Personal Travel Eco
25075 satisfied Female Loyal Customer 63 Personal Travel Eco
25081 satisfied Female Loyal Customer 30 Personal Travel Eco
25084 dissatisfied Male Loyal Customer 44 Personal Travel Eco Plus
25176 satisfied Female Loyal Customer 12 Personal Travel Eco
25180 satisfied Female Loyal Customer 30 Personal Travel Eco Plus
25207 satisfied Female Loyal Customer 60 Personal Travel Eco
25219 satisfied Female Loyal Customer 19 Personal Travel Eco
25240 satisfied Female Loyal Customer 21 Personal Travel Eco
25294 satisfied Female Loyal Customer 34 Personal Travel Eco
25297 dissatisfied Male Loyal Customer 50 Personal Travel Eco
25299 dissatisfied Male Loyal Customer 14 Personal Travel Eco
25311 dissatisfied Male Loyal Customer 38 Personal Travel Eco Plus
25313 dissatisfied Male Loyal Customer 46 Personal Travel Eco
25316 dissatisfied Male Loyal Customer 25 Personal Travel Eco Plus
25354 satisfied Female Loyal Customer 53 Personal Travel Eco
25365 satisfied Female Loyal Customer 64 Personal Travel Eco Plus
25379 satisfied Female Loyal Customer 25 Personal Travel Eco
25382 dissatisfied Male Loyal Customer 30 Personal Travel Eco
25387 satisfied Female Loyal Customer 65 Personal Travel Eco
25400 satisfied Female Loyal Customer 35 Personal Travel Eco Plus
25420 satisfied Female Loyal Customer 67 Personal Travel Eco
25428 satisfied Female Loyal Customer 7 Personal Travel Eco
25448 dissatisfied Male Loyal Customer 66 Personal Travel Eco
25462 satisfied Female Loyal Customer 15 Personal Travel Eco
25476 satisfied Female Loyal Customer 14 Personal Travel Eco
25509 satisfied Female Loyal Customer 9 Personal Travel Eco Plus
25565 dissatisfied Male Loyal Customer 45 Personal Travel Eco
25576 dissatisfied Male Loyal Customer 47 Personal Travel Eco
25577 satisfied Female Loyal Customer 31 Personal Travel Eco Plus
25583 satisfied Female Loyal Customer 42 Personal Travel Eco
25587 satisfied Female Loyal Customer 28 Personal Travel Eco
25619 dissatisfied Male Loyal Customer 56 Personal Travel Eco
25631 satisfied Female Loyal Customer 10 Personal Travel Eco
25641 dissatisfied Male Loyal Customer 26 Personal Travel Business
25643 satisfied Female Loyal Customer 38 Personal Travel Eco
25647 dissatisfied Male Loyal Customer 23 Personal Travel Business
25651 satisfied Female Loyal Customer 28 Personal Travel Eco
25664 dissatisfied Male Loyal Customer 58 Personal Travel Eco
25668 satisfied Female Loyal Customer 9 Personal Travel Eco
25720 satisfied Female Loyal Customer 26 Personal Travel Eco
25746 dissatisfied Male Loyal Customer 45 Personal Travel Eco
25764 satisfied Female Loyal Customer 38 Personal Travel Eco
25785 dissatisfied Male Loyal Customer 40 Personal Travel Eco
25791 dissatisfied Male Loyal Customer 14 Personal Travel Business
25813 dissatisfied Male Loyal Customer 20 Personal Travel Eco
25825 satisfied Female Loyal Customer 45 Personal Travel Eco
25838 dissatisfied Male Loyal Customer 60 Personal Travel Eco Plus
25841 satisfied Female Loyal Customer 8 Personal Travel Eco
25847 dissatisfied Male Loyal Customer 58 Personal Travel Eco
25855 dissatisfied Male Loyal Customer 31 Personal Travel Eco Plus
25900 satisfied Female Loyal Customer 47 Personal Travel Eco
25925 satisfied Female Loyal Customer 66 Personal Travel Eco
25927 satisfied Female Loyal Customer 55 Personal Travel Eco
25942 satisfied Female Loyal Customer 32 Personal Travel Eco
25953 satisfied Female Loyal Customer 60 Personal Travel Eco
25956 dissatisfied Male Loyal Customer 67 Personal Travel Eco
25959 satisfied Female Loyal Customer 37 Personal Travel Eco
25963 satisfied Female Loyal Customer 28 Personal Travel Eco
25985 satisfied Female Loyal Customer 32 Personal Travel Eco
25993 dissatisfied Male Loyal Customer 59 Personal Travel Eco
26006 satisfied Female Loyal Customer 24 Personal Travel Eco
26024 dissatisfied Male Loyal Customer 34 Personal Travel Eco
26025 satisfied Female Loyal Customer 67 Personal Travel Eco Plus
26030 satisfied Female Loyal Customer 28 Personal Travel Eco
26036 dissatisfied Male Loyal Customer 46 Personal Travel Eco
26069 dissatisfied Male Loyal Customer 32 Personal Travel Business
26080 dissatisfied Male Loyal Customer 29 Personal Travel Eco
26083 dissatisfied Male Loyal Customer 36 Personal Travel Eco
26103 dissatisfied Male Loyal Customer 65 Personal Travel Eco
26125 satisfied Female Loyal Customer 46 Personal Travel Eco
26144 satisfied Female Loyal Customer 55 Personal Travel Eco Plus
26184 dissatisfied Male Loyal Customer 31 Personal Travel Eco
26185 satisfied Female Loyal Customer 24 Personal Travel Eco Plus
26188 dissatisfied Male Loyal Customer 66 Personal Travel Eco
26193 dissatisfied Male Loyal Customer 25 Personal Travel Business
26203 satisfied Female Loyal Customer 43 Personal Travel Eco
26232 satisfied Female Loyal Customer 65 Personal Travel Eco
26255 satisfied Female Loyal Customer 20 Personal Travel Eco
26280 satisfied Female Loyal Customer 52 Personal Travel Eco
26287 satisfied Female Loyal Customer 10 Personal Travel Eco
26292 dissatisfied Male Loyal Customer 50 Personal Travel Eco
26304 satisfied Female Loyal Customer 43 Personal Travel Eco
26316 dissatisfied Male Loyal Customer 61 Personal Travel Eco
26341 dissatisfied Male Loyal Customer 42 Personal Travel Eco Plus
26350 satisfied Female Loyal Customer 31 Personal Travel Eco
26362 satisfied Female Loyal Customer 18 Personal Travel Eco
26364 satisfied Female Loyal Customer 19 Personal Travel Eco
26375 satisfied Female Loyal Customer 25 Personal Travel Eco Plus
26379 satisfied Female Loyal Customer 62 Personal Travel Eco
26389 satisfied Female Loyal Customer 56 Personal Travel Eco Plus
26395 dissatisfied Male Loyal Customer 48 Personal Travel Business
26402 satisfied Female Loyal Customer 49 Personal Travel Eco
26407 dissatisfied Male Loyal Customer 15 Personal Travel Eco
26412 dissatisfied Male Loyal Customer 68 Personal Travel Eco
26438 satisfied Female Loyal Customer 21 Personal Travel Eco
26453 dissatisfied Male Loyal Customer 35 Personal Travel Eco
26472 dissatisfied Male Loyal Customer 13 Personal Travel Eco
26496 dissatisfied Male Loyal Customer 41 Personal Travel Eco
26497 dissatisfied Male Loyal Customer 14 Personal Travel Eco
26511 satisfied Female Loyal Customer 17 Personal Travel Eco
26515 dissatisfied Male Loyal Customer 24 Personal Travel Business
26522 dissatisfied Male Loyal Customer 13 Personal Travel Eco
26547 satisfied Female Loyal Customer 36 Personal Travel Eco
26555 satisfied Female Loyal Customer 59 Personal Travel Eco
26574 satisfied Female Loyal Customer 37 Personal Travel Eco Plus
26594 satisfied Female Loyal Customer 55 Personal Travel Eco
26605 satisfied Female Loyal Customer 28 Personal Travel Eco
26628 satisfied Female Loyal Customer 63 Personal Travel Eco
26675 satisfied Female Loyal Customer 49 Personal Travel Business
26687 dissatisfied Male Loyal Customer 42 Personal Travel Eco
26703 satisfied Female Loyal Customer 52 Personal Travel Eco
26722 dissatisfied Male Loyal Customer 48 Personal Travel Eco
26748 dissatisfied Male Loyal Customer 34 Personal Travel Eco
26749 satisfied Female Loyal Customer 46 Personal Travel Eco
26773 satisfied Female Loyal Customer 34 Personal Travel Eco
26774 dissatisfied Male Loyal Customer 47 Personal Travel Eco
26791 satisfied Female Loyal Customer 12 Personal Travel Eco
26808 dissatisfied Male Loyal Customer 29 Personal Travel Eco
26821 satisfied Female Loyal Customer 30 Personal Travel Eco Plus
26840 dissatisfied Male Loyal Customer 57 Personal Travel Eco Plus
26862 dissatisfied Male Loyal Customer 49 Personal Travel Eco
26867 satisfied Female Loyal Customer 30 Personal Travel Eco
26868 satisfied Female Loyal Customer 39 Personal Travel Eco
26884 satisfied Female Loyal Customer 21 Personal Travel Eco
26894 satisfied Female Loyal Customer 50 Personal Travel Eco
26896 dissatisfied Male Loyal Customer 25 Personal Travel Eco
26902 dissatisfied Male Loyal Customer 10 Personal Travel Eco
26914 satisfied Female Loyal Customer 20 Personal Travel Eco
26919 satisfied Female Loyal Customer 68 Personal Travel Eco
26924 satisfied Female Loyal Customer 60 Personal Travel Eco
26939 dissatisfied Male Loyal Customer 40 Personal Travel Business
26941 dissatisfied Male Loyal Customer 40 Personal Travel Eco
26951 satisfied Female Loyal Customer 49 Personal Travel Eco Plus
26953 satisfied Female Loyal Customer 66 Personal Travel Eco
26975 dissatisfied Male Loyal Customer 16 Personal Travel Eco
26978 satisfied Female Loyal Customer 14 Personal Travel Eco
27020 dissatisfied Male Loyal Customer 22 Personal Travel Eco
27022 satisfied Female Loyal Customer 43 Personal Travel Eco
27035 satisfied Female Loyal Customer 48 Personal Travel Business
27039 dissatisfied Male Loyal Customer 9 Personal Travel Eco
27058 satisfied Female Loyal Customer 35 Personal Travel Eco Plus
27092 satisfied Female Loyal Customer 27 Personal Travel Eco
27096 dissatisfied Male Loyal Customer 8 Personal Travel Eco Plus
27128 satisfied Female Loyal Customer 50 Personal Travel Eco
27130 dissatisfied Male Loyal Customer 39 Personal Travel Eco
27144 satisfied Female Loyal Customer 14 Personal Travel Eco
27147 dissatisfied Male Loyal Customer 24 Personal Travel Eco
27162 dissatisfied Male Loyal Customer 40 Personal Travel Eco
27189 satisfied Female Loyal Customer 51 Personal Travel Eco Plus
27255 dissatisfied Male Loyal Customer 38 Personal Travel Eco
27277 satisfied Female Loyal Customer 8 Personal Travel Eco
27326 satisfied Female Loyal Customer 29 Personal Travel Eco
27327 satisfied Female Loyal Customer 9 Personal Travel Eco Plus
27350 dissatisfied Male Loyal Customer 62 Personal Travel Eco Plus
27351 satisfied Female Loyal Customer 69 Personal Travel Eco
27362 dissatisfied Male Loyal Customer 52 Personal Travel Eco
27369 satisfied Female Loyal Customer 62 Personal Travel Eco
27371 satisfied Female Loyal Customer 67 Personal Travel Eco
27375 dissatisfied Male Loyal Customer 69 Personal Travel Eco
27404 dissatisfied Male Loyal Customer 31 Personal Travel Eco
27412 dissatisfied Male Loyal Customer 43 Personal Travel Eco
27438 satisfied Female Loyal Customer 44 Personal Travel Eco
27482 satisfied Female Loyal Customer 68 Personal Travel Eco
27483 dissatisfied Male Loyal Customer 27 Personal Travel Eco
27502 satisfied Female Loyal Customer 40 Personal Travel Eco
27523 dissatisfied Male Loyal Customer 36 Personal Travel Eco
27524 dissatisfied Male Loyal Customer 41 Personal Travel Eco
27575 dissatisfied Female Loyal Customer 65 Personal Travel Eco
27583 satisfied Female Loyal Customer 55 Personal Travel Eco Plus
27608 dissatisfied Male Loyal Customer 19 Personal Travel Eco
27614 satisfied Female Loyal Customer 31 Personal Travel Eco
27644 satisfied Female Loyal Customer 20 Personal Travel Eco
27654 satisfied Female Loyal Customer 17 Personal Travel Eco
27660 satisfied Female Loyal Customer 53 Personal Travel Eco
27662 dissatisfied Male Loyal Customer 58 Personal Travel Eco
27671 dissatisfied Male Loyal Customer 62 Personal Travel Business
27714 satisfied Female Loyal Customer 21 Personal Travel Eco
27715 dissatisfied Male Loyal Customer 54 Personal Travel Eco
27716 satisfied Female Loyal Customer 29 Personal Travel Eco
27743 satisfied Female Loyal Customer 22 Personal Travel Eco Plus
27744 satisfied Female Loyal Customer 40 Personal Travel Business
27749 satisfied Female Loyal Customer 60 Personal Travel Eco
27779 dissatisfied Male Loyal Customer 17 Personal Travel Eco
27781 satisfied Female Loyal Customer 22 Personal Travel Eco
27793 satisfied Female Loyal Customer 20 Personal Travel Eco
27796 dissatisfied Female Loyal Customer 18 Personal Travel Eco
27800 satisfied Female Loyal Customer 59 Personal Travel Eco
27811 satisfied Female Loyal Customer 12 Personal Travel Eco
27817 satisfied Female Loyal Customer 40 Personal Travel Eco Plus
27823 satisfied Female Loyal Customer 17 Personal Travel Business
27845 satisfied Female Loyal Customer 23 Personal Travel Eco
27847 satisfied Female Loyal Customer 52 Personal Travel Eco Plus
27862 dissatisfied Male Loyal Customer 49 Personal Travel Eco
27879 satisfied Female Loyal Customer 42 Personal Travel Eco
27880 satisfied Female Loyal Customer 13 Personal Travel Eco
27888 dissatisfied Male Loyal Customer 10 Personal Travel Eco
27907 dissatisfied Male Loyal Customer 11 Personal Travel Eco Plus
27949 dissatisfied Male Loyal Customer 58 Personal Travel Eco
27955 dissatisfied Male Loyal Customer 64 Personal Travel Eco
27956 dissatisfied Male Loyal Customer 18 Personal Travel Eco
27981 dissatisfied Male Loyal Customer 32 Personal Travel Eco Plus
27985 dissatisfied Male Loyal Customer 49 Personal Travel Eco
27990 dissatisfied Male Loyal Customer 60 Personal Travel Eco
27994 dissatisfied Male Loyal Customer 8 Personal Travel Eco
28008 satisfied Female Loyal Customer 49 Personal Travel Eco
28035 satisfied Female Loyal Customer 26 Personal Travel Eco
28036 dissatisfied Male Loyal Customer 18 Personal Travel Business
28038 dissatisfied Male Loyal Customer 62 Personal Travel Eco
28056 dissatisfied Male Loyal Customer 52 Personal Travel Eco
28060 dissatisfied Male Loyal Customer 20 Personal Travel Eco Plus
28072 dissatisfied Male Loyal Customer 44 Personal Travel Eco
28073 satisfied Female Loyal Customer 8 Personal Travel Eco
28077 satisfied Female Loyal Customer 21 Personal Travel Eco
28079 satisfied Female Loyal Customer 42 Personal Travel Eco
28089 dissatisfied Male Loyal Customer 64 Personal Travel Eco
28135 dissatisfied Male Loyal Customer 49 Personal Travel Eco
28144 satisfied Female Loyal Customer 48 Personal Travel Eco
28154 dissatisfied Male Loyal Customer 50 Personal Travel Eco
28163 dissatisfied Male Loyal Customer 62 Personal Travel Business
28180 dissatisfied Male Loyal Customer 12 Personal Travel Eco
28197 dissatisfied Male Loyal Customer 38 Personal Travel Eco Plus
28199 satisfied Female Loyal Customer 19 Personal Travel Eco
28202 dissatisfied Male Loyal Customer 68 Personal Travel Eco
28229 satisfied Female Loyal Customer 14 Personal Travel Eco
28230 satisfied Female Loyal Customer 21 Personal Travel Eco
28234 satisfied Female Loyal Customer 67 Personal Travel Business
28242 dissatisfied Male Loyal Customer 36 Personal Travel Eco
28245 satisfied Female Loyal Customer 18 Personal Travel Eco
28248 dissatisfied Male Loyal Customer 9 Personal Travel Eco Plus
28250 dissatisfied Male Loyal Customer 27 Personal Travel Eco
28252 dissatisfied Male Loyal Customer 36 Personal Travel Eco
28261 dissatisfied Male Loyal Customer 48 Personal Travel Eco
28370 satisfied Female Loyal Customer 37 Personal Travel Eco
28378 dissatisfied Male Loyal Customer 31 Personal Travel Eco
28380 dissatisfied Male Loyal Customer 60 Personal Travel Eco
28385 satisfied Female Loyal Customer 44 Personal Travel Eco Plus
28406 satisfied Female Loyal Customer 67 Personal Travel Eco
28437 dissatisfied Male Loyal Customer 36 Personal Travel Eco
28456 dissatisfied Male Loyal Customer 37 Personal Travel Eco
28472 dissatisfied Male Loyal Customer 16 Personal Travel Eco
28480 satisfied Female Loyal Customer 67 Personal Travel Eco
28496 satisfied Female Loyal Customer 58 Personal Travel Eco Plus
28501 satisfied Female Loyal Customer 27 Personal Travel Eco
28503 dissatisfied Male Loyal Customer 36 Personal Travel Eco
28504 satisfied Female Loyal Customer 19 Personal Travel Eco
28515 satisfied Female Loyal Customer 15 Personal Travel Eco
28534 satisfied Female Loyal Customer 31 Personal Travel Eco
28539 dissatisfied Male Loyal Customer 38 Personal Travel Eco
28559 dissatisfied Male Loyal Customer 68 Personal Travel Eco Plus
28563 satisfied Female Loyal Customer 35 Personal Travel Eco
28567 satisfied Female Loyal Customer 25 Personal Travel Eco
28569 satisfied Female Loyal Customer 27 Personal Travel Eco
28581 satisfied Female Loyal Customer 42 Personal Travel Business
28611 dissatisfied Male Loyal Customer 48 Personal Travel Eco
28623 satisfied Female Loyal Customer 44 Personal Travel Eco
28625 satisfied Female Loyal Customer 37 Personal Travel Eco
28633 dissatisfied Male Loyal Customer 60 Personal Travel Eco Plus
28637 satisfied Female Loyal Customer 12 Personal Travel Eco
28648 dissatisfied Male Loyal Customer 70 Personal Travel Eco
28655 dissatisfied Male Loyal Customer 68 Personal Travel Eco
28664 dissatisfied Female Loyal Customer 46 Personal Travel Eco
28691 satisfied Female Loyal Customer 32 Personal Travel Business
28710 dissatisfied Male Loyal Customer 23 Personal Travel Eco
28716 satisfied Female Loyal Customer 14 Personal Travel Eco
28721 dissatisfied Male Loyal Customer 31 Personal Travel Eco
28729 satisfied Female Loyal Customer 24 Personal Travel Eco
28739 satisfied Female Loyal Customer 60 Personal Travel Eco
28751 satisfied Female Loyal Customer 49 Personal Travel Eco
28755 satisfied Female Loyal Customer 21 Personal Travel Eco Plus
28767 satisfied Female Loyal Customer 66 Personal Travel Eco
28812 satisfied Female Loyal Customer 52 Personal Travel Eco
28822 satisfied Female Loyal Customer 11 Personal Travel Eco
28825 satisfied Female Loyal Customer 39 Personal Travel Eco
28826 satisfied Female Loyal Customer 51 Personal Travel Eco
28836 satisfied Female Loyal Customer 11 Personal Travel Eco
28839 satisfied Female Loyal Customer 9 Personal Travel Eco
28888 satisfied Female Loyal Customer 40 Personal Travel Eco Plus
28896 dissatisfied Male Loyal Customer 18 Personal Travel Eco
28905 dissatisfied Male Loyal Customer 32 Personal Travel Eco
28924 dissatisfied Male Loyal Customer 48 Personal Travel Eco Plus
28936 dissatisfied Male Loyal Customer 16 Personal Travel Eco
28962 satisfied Female Loyal Customer 59 Personal Travel Eco
28965 dissatisfied Female Loyal Customer 11 Personal Travel Eco Plus
28974 satisfied Female Loyal Customer 48 Personal Travel Eco
28977 satisfied Female Loyal Customer 22 Personal Travel Eco
28988 satisfied Female Loyal Customer 30 Personal Travel Eco Plus
28998 satisfied Female Loyal Customer 62 Personal Travel Eco Plus
29009 dissatisfied Male Loyal Customer 14 Personal Travel Eco Plus
29059 satisfied Female Loyal Customer 55 Personal Travel Eco
29072 dissatisfied Male Loyal Customer 9 Personal Travel Eco
29081 dissatisfied Male Loyal Customer 40 Personal Travel Eco
29097 dissatisfied Male Loyal Customer 34 Personal Travel Eco
29099 satisfied Female Loyal Customer 41 Personal Travel Eco Plus
29102 dissatisfied Male Loyal Customer 57 Personal Travel Eco Plus
29111 dissatisfied Male Loyal Customer 13 Personal Travel Eco
29138 dissatisfied Male Loyal Customer 40 Personal Travel Eco
29152 dissatisfied Male Loyal Customer 15 Personal Travel Eco Plus
29162 dissatisfied Male Loyal Customer 41 Personal Travel Eco
29251 satisfied Female Loyal Customer 55 Personal Travel Eco
29260 satisfied Female Loyal Customer 31 Personal Travel Eco
29271 satisfied Female Loyal Customer 60 Personal Travel Eco
29287 satisfied Female Loyal Customer 17 Personal Travel Eco
29290 satisfied Female Loyal Customer 9 Personal Travel Business
29303 satisfied Female Loyal Customer 25 Personal Travel Eco
29319 satisfied Female Loyal Customer 41 Personal Travel Eco
29322 satisfied Female Loyal Customer 56 Personal Travel Eco
29326 satisfied Female Loyal Customer 70 Personal Travel Eco
29341 satisfied Female Loyal Customer 59 Personal Travel Eco
29343 dissatisfied Male Loyal Customer 70 Personal Travel Eco
29348 dissatisfied Male Loyal Customer 23 Personal Travel Eco
29350 dissatisfied Male Loyal Customer 52 Personal Travel Eco
29359 satisfied Female Loyal Customer 48 Personal Travel Eco
29388 satisfied Female Loyal Customer 37 Personal Travel Eco Plus
29395 satisfied Female Loyal Customer 30 Personal Travel Eco
29396 dissatisfied Male Loyal Customer 32 Personal Travel Eco
29410 satisfied Female Loyal Customer 42 Personal Travel Eco
29437 dissatisfied Male Loyal Customer 52 Personal Travel Eco
29455 dissatisfied Male Loyal Customer 54 Personal Travel Eco
29457 dissatisfied Male Loyal Customer 48 Personal Travel Eco
29463 dissatisfied Male Loyal Customer 27 Personal Travel Eco
29468 satisfied Female Loyal Customer 11 Personal Travel Eco
29475 satisfied Female Loyal Customer 13 Personal Travel Eco Plus
29498 dissatisfied Male Loyal Customer 58 Personal Travel Eco Plus
29519 dissatisfied Male Loyal Customer 29 Personal Travel Eco
29521 satisfied Female Loyal Customer 17 Personal Travel Eco
29523 dissatisfied Male Loyal Customer 69 Personal Travel Eco
29536 satisfied Female Loyal Customer 30 Personal Travel Eco
29545 satisfied Female Loyal Customer 24 Personal Travel Eco
29548 satisfied Female Loyal Customer 68 Personal Travel Eco Plus
29561 satisfied Female Loyal Customer 39 Personal Travel Eco
29576 dissatisfied Male Loyal Customer 55 Personal Travel Eco
29592 satisfied Female Loyal Customer 52 Personal Travel Eco
29644 dissatisfied Male Loyal Customer 30 Personal Travel Business
29676 dissatisfied Male Loyal Customer 57 Personal Travel Eco
29692 dissatisfied Male Loyal Customer 10 Personal Travel Eco
29709 satisfied Female Loyal Customer 27 Personal Travel Eco
29716 satisfied Female Loyal Customer 38 Personal Travel Eco Plus
29759 satisfied Female Loyal Customer 28 Personal Travel Business
29770 dissatisfied Male Loyal Customer 16 Personal Travel Eco
29795 satisfied Female Loyal Customer 55 Personal Travel Eco
29798 dissatisfied Male Loyal Customer 52 Personal Travel Eco
29801 dissatisfied Male Loyal Customer 53 Personal Travel Eco
29804 satisfied Female Loyal Customer 36 Personal Travel Eco
29807 dissatisfied Male Loyal Customer 42 Personal Travel Eco
29846 satisfied Female Loyal Customer 10 Personal Travel Eco
29853 dissatisfied Male Loyal Customer 53 Personal Travel Eco
29871 satisfied Female Loyal Customer 24 Personal Travel Eco Plus
29909 dissatisfied Male Loyal Customer 24 Personal Travel Eco
29939 satisfied Female Loyal Customer 15 Personal Travel Eco Plus
29946 satisfied Female Loyal Customer 25 Personal Travel Eco
29948 dissatisfied Male Loyal Customer 38 Personal Travel Eco
29955 dissatisfied Male Loyal Customer 39 Personal Travel Eco Plus
29970 dissatisfied Male Loyal Customer 34 Personal Travel Eco
29994 dissatisfied Male Loyal Customer 8 Personal Travel Eco
30005 satisfied Female Loyal Customer 19 Personal Travel Eco
30031 dissatisfied Male Loyal Customer 28 Personal Travel Eco
30033 dissatisfied Male Loyal Customer 48 Personal Travel Eco
30040 dissatisfied Male Loyal Customer 48 Personal Travel Eco
30098 satisfied Female Loyal Customer 18 Personal Travel Eco
30101 satisfied Female Loyal Customer 32 Personal Travel Eco
30102 dissatisfied Male Loyal Customer 13 Personal Travel Eco
30121 satisfied Female Loyal Customer 27 Personal Travel Eco
30123 dissatisfied Male Loyal Customer 52 Personal Travel Eco
30124 dissatisfied Male Loyal Customer 56 Personal Travel Eco
30127 dissatisfied Male Loyal Customer 59 Personal Travel Eco
30136 satisfied Female Loyal Customer 14 Personal Travel Business
30151 satisfied Female Loyal Customer 42 Personal Travel Eco
30176 dissatisfied Male Loyal Customer 12 Personal Travel Eco
30182 satisfied Female Loyal Customer 10 Personal Travel Eco
30185 satisfied Female Loyal Customer 8 Personal Travel Eco
30201 satisfied Female Loyal Customer 18 Personal Travel Eco
30209 dissatisfied Male Loyal Customer 30 Personal Travel Eco
30215 dissatisfied Male Loyal Customer 14 Personal Travel Eco
30217 satisfied Female Loyal Customer 12 Personal Travel Eco
30224 dissatisfied Male Loyal Customer 30 Personal Travel Eco
30227 satisfied Female Loyal Customer 14 Personal Travel Eco
30230 dissatisfied Male Loyal Customer 27 Personal Travel Eco
30247 satisfied Female Loyal Customer 21 Personal Travel Eco
30258 satisfied Female Loyal Customer 70 Personal Travel Eco
30279 dissatisfied Male Loyal Customer 8 Personal Travel Eco
30304 satisfied Female Loyal Customer 58 Personal Travel Eco Plus
30318 dissatisfied Male Loyal Customer 62 Personal Travel Eco
30326 satisfied Female Loyal Customer 58 Personal Travel Eco Plus
30383 dissatisfied Male Loyal Customer 62 Personal Travel Eco
30400 satisfied Female Loyal Customer 47 Personal Travel Eco
30405 satisfied Female Loyal Customer 59 Personal Travel Eco
30419 dissatisfied Male Loyal Customer 66 Personal Travel Eco
30423 dissatisfied Male Loyal Customer 46 Personal Travel Eco
30428 dissatisfied Male Loyal Customer 41 Personal Travel Business
30431 satisfied Female Loyal Customer 50 Personal Travel Eco
30432 satisfied Female Loyal Customer 44 Personal Travel Eco Plus
30441 dissatisfied Male Loyal Customer 37 Personal Travel Eco
30450 satisfied Female Loyal Customer 49 Personal Travel Eco
30455 dissatisfied Male Loyal Customer 61 Personal Travel Eco
30460 dissatisfied Male Loyal Customer 42 Personal Travel Eco
30470 satisfied Female Loyal Customer 70 Personal Travel Eco
30491 satisfied Female Loyal Customer 67 Personal Travel Eco
30493 dissatisfied Male Loyal Customer 24 Personal Travel Eco
30519 satisfied Female Loyal Customer 48 Personal Travel Eco
30525 satisfied Female Loyal Customer 20 Personal Travel Eco
30532 satisfied Female Loyal Customer 12 Personal Travel Business
30536 dissatisfied Male Loyal Customer 62 Personal Travel Eco
30574 satisfied Female Loyal Customer 46 Personal Travel Eco
30607 satisfied Female Loyal Customer 19 Personal Travel Eco
30609 satisfied Female Loyal Customer 43 Personal Travel Eco
30616 satisfied Female Loyal Customer 34 Personal Travel Eco
30645 dissatisfied Male Loyal Customer 56 Personal Travel Eco
30666 dissatisfied Male Loyal Customer 8 Personal Travel Eco
30703 dissatisfied Male Loyal Customer 35 Personal Travel Eco
30715 dissatisfied Male Loyal Customer 35 Personal Travel Eco Plus
30723 satisfied Female Loyal Customer 58 Personal Travel Eco
30747 dissatisfied Male Loyal Customer 39 Personal Travel Eco
30753 satisfied Female Loyal Customer 14 Personal Travel Eco
30767 satisfied Female Loyal Customer 18 Personal Travel Eco Plus
30787 dissatisfied Male Loyal Customer 45 Personal Travel Eco
30798 dissatisfied Male Loyal Customer 57 Personal Travel Eco
30800 satisfied Female Loyal Customer 7 Personal Travel Eco
30807 dissatisfied Male Loyal Customer 14 Personal Travel Eco
30818 dissatisfied Male Loyal Customer 24 Personal Travel Eco
30829 dissatisfied Female Loyal Customer 66 Personal Travel Eco
30838 dissatisfied Male Loyal Customer 64 Personal Travel Eco
30861 dissatisfied Male Loyal Customer 33 Personal Travel Eco
30866 dissatisfied Female Loyal Customer 13 Personal Travel Eco
30877 dissatisfied Female Loyal Customer 41 Personal Travel Business
30879 dissatisfied Male Loyal Customer 68 Personal Travel Eco
30897 dissatisfied Male Loyal Customer 8 Personal Travel Eco
30920 dissatisfied Female Loyal Customer 45 Personal Travel Eco
30966 dissatisfied Male Loyal Customer 40 Personal Travel Eco
31001 dissatisfied Male Loyal Customer 7 Personal Travel Eco Plus
31007 dissatisfied Male Loyal Customer 67 Personal Travel Eco
31050 dissatisfied Female Loyal Customer 64 Personal Travel Eco
31051 dissatisfied Female Loyal Customer 15 Personal Travel Business
31056 dissatisfied Female Loyal Customer 7 Personal Travel Eco
31066 dissatisfied Female Loyal Customer 7 Personal Travel Eco
31074 dissatisfied Female Loyal Customer 28 Personal Travel Eco
31093 dissatisfied Female Loyal Customer 15 Personal Travel Eco
31099 dissatisfied Male Loyal Customer 65 Personal Travel Eco
31110 dissatisfied Male Loyal Customer 37 Personal Travel Eco Plus
31112 dissatisfied Female Loyal Customer 10 Personal Travel Eco
31113 dissatisfied Female Loyal Customer 23 Personal Travel Eco
31128 dissatisfied Male Loyal Customer 65 Personal Travel Business
31134 dissatisfied Male Loyal Customer 34 Personal Travel Eco
31201 dissatisfied Female Loyal Customer 16 Personal Travel Eco
31202 dissatisfied Female Loyal Customer 14 Personal Travel Eco
31225 dissatisfied Female Loyal Customer 44 Personal Travel Eco
31228 dissatisfied Male Loyal Customer 27 Personal Travel Eco
31233 dissatisfied Female Loyal Customer 55 Personal Travel Eco
31234 dissatisfied Female Loyal Customer 38 Personal Travel Business
31241 dissatisfied Female Loyal Customer 44 Personal Travel Eco
31260 dissatisfied Female Loyal Customer 49 Personal Travel Eco
31279 dissatisfied Male Loyal Customer 16 Personal Travel Eco Plus
31281 dissatisfied Female Loyal Customer 20 Personal Travel Eco
31296 dissatisfied Female Loyal Customer 61 Personal Travel Eco Plus
31297 dissatisfied Male Loyal Customer 23 Personal Travel Eco Plus
31298 dissatisfied Male Loyal Customer 20 Personal Travel Eco
31300 dissatisfied Male Loyal Customer 9 Personal Travel Eco
31305 dissatisfied Male Loyal Customer 13 Personal Travel Eco
31324 dissatisfied Male Loyal Customer 55 Personal Travel Eco
31337 dissatisfied Male Loyal Customer 68 Personal Travel Eco
31360 dissatisfied Female Loyal Customer 31 Personal Travel Eco
31367 dissatisfied Female Loyal Customer 66 Personal Travel Eco
31395 dissatisfied Female Loyal Customer 43 Personal Travel Eco
31396 dissatisfied Female Loyal Customer 59 Personal Travel Eco
31401 dissatisfied Male Loyal Customer 52 Personal Travel Eco Plus
31409 dissatisfied Female Loyal Customer 63 Personal Travel Eco Plus
31437 dissatisfied Female Loyal Customer 61 Personal Travel Eco
31494 dissatisfied Male Loyal Customer 44 Personal Travel Eco
31501 dissatisfied Female Loyal Customer 42 Personal Travel Eco Plus
31503 dissatisfied Male Loyal Customer 15 Personal Travel Eco Plus
31522 dissatisfied Male Loyal Customer 47 Personal Travel Eco
31526 dissatisfied Female Loyal Customer 16 Personal Travel Business
31534 dissatisfied Female Loyal Customer 63 Personal Travel Eco
31559 dissatisfied Male Loyal Customer 44 Personal Travel Eco
31566 dissatisfied Male Loyal Customer 50 Personal Travel Eco
31572 dissatisfied Male Loyal Customer 58 Personal Travel Eco
31603 dissatisfied Female Loyal Customer 30 Personal Travel Eco
31604 dissatisfied Female Loyal Customer 17 Personal Travel Eco
31616 dissatisfied Female Loyal Customer 52 Personal Travel Eco
31618 dissatisfied Female Loyal Customer 55 Personal Travel Eco
31619 dissatisfied Male Loyal Customer 21 Personal Travel Business
31626 dissatisfied Female Loyal Customer 10 Personal Travel Eco Plus
31663 dissatisfied Male Loyal Customer 31 Personal Travel Eco
31664 dissatisfied Male Loyal Customer 24 Personal Travel Eco
31670 dissatisfied Female Loyal Customer 64 Personal Travel Eco
31676 dissatisfied Female Loyal Customer 49 Personal Travel Eco
31693 dissatisfied Male Loyal Customer 31 Personal Travel Eco
31711 dissatisfied Female Loyal Customer 48 Personal Travel Eco Plus
31768 dissatisfied Male Loyal Customer 50 Personal Travel Eco
31797 dissatisfied Female Loyal Customer 54 Personal Travel Business
31800 dissatisfied Male Loyal Customer 51 Personal Travel Eco
31808 dissatisfied Male Loyal Customer 23 Personal Travel Eco
31809 dissatisfied Female Loyal Customer 40 Personal Travel Eco
31815 dissatisfied Female Loyal Customer 47 Personal Travel Eco
31845 dissatisfied Female Loyal Customer 62 Personal Travel Eco
31867 dissatisfied Female Loyal Customer 12 Personal Travel Eco
31875 dissatisfied Female Loyal Customer 33 Personal Travel Eco
31878 dissatisfied Female Loyal Customer 69 Personal Travel Eco
31888 dissatisfied Female Loyal Customer 33 Personal Travel Eco
31912 dissatisfied Male Loyal Customer 38 Personal Travel Eco
31918 dissatisfied Female Loyal Customer 69 Personal Travel Eco
31959 dissatisfied Male Loyal Customer 66 Personal Travel Business
31985 dissatisfied Female Loyal Customer 43 Personal Travel Eco
32002 dissatisfied Female Loyal Customer 48 Personal Travel Eco
32025 dissatisfied Female Loyal Customer 65 Personal Travel Eco
32035 dissatisfied Female Loyal Customer 56 Personal Travel Eco
32065 dissatisfied Male Loyal Customer 41 Personal Travel Eco
32085 satisfied Male Loyal Customer 17 Personal Travel Eco
32110 dissatisfied Female Loyal Customer 50 Personal Travel Eco
32128 satisfied Female Loyal Customer 8 Personal Travel Eco
32137 dissatisfied Female Loyal Customer 33 Personal Travel Eco
32146 satisfied Female Loyal Customer 27 Personal Travel Eco
32209 dissatisfied Male Loyal Customer 57 Personal Travel Eco
32214 dissatisfied Female Loyal Customer 8 Personal Travel Eco
32218 dissatisfied Male Loyal Customer 67 Personal Travel Eco
32219 satisfied Male Loyal Customer 7 Personal Travel Eco
32234 dissatisfied Male Loyal Customer 35 Personal Travel Eco
32237 dissatisfied Male Loyal Customer 35 Personal Travel Eco
32251 dissatisfied Female Loyal Customer 46 Personal Travel Eco Plus
32259 dissatisfied Male Loyal Customer 46 Personal Travel Eco
32273 dissatisfied Female Loyal Customer 39 Personal Travel Eco
32281 satisfied Male Loyal Customer 47 Personal Travel Eco Plus
32292 dissatisfied Female Loyal Customer 54 Personal Travel Eco
32300 satisfied Female Loyal Customer 67 Personal Travel Eco
32331 dissatisfied Male Loyal Customer 28 Personal Travel Eco
32334 satisfied Male Loyal Customer 28 Personal Travel Eco
32343 dissatisfied Female Loyal Customer 16 Personal Travel Eco
32354 dissatisfied Male Loyal Customer 24 Personal Travel Eco
32371 dissatisfied Female Loyal Customer 18 Personal Travel Eco
32409 dissatisfied Female Loyal Customer 57 Personal Travel Eco Plus
32411 dissatisfied Male Loyal Customer 60 Personal Travel Eco
32413 dissatisfied Male Loyal Customer 65 Personal Travel Eco Plus
32419 dissatisfied Female Loyal Customer 28 Personal Travel Business
32421 dissatisfied Male Loyal Customer 14 Personal Travel Eco
32423 dissatisfied Female Loyal Customer 50 Personal Travel Eco
32431 dissatisfied Male Loyal Customer 43 Personal Travel Business
32447 satisfied Male Loyal Customer 42 Personal Travel Eco
32483 dissatisfied Male Loyal Customer 56 Personal Travel Eco
32486 satisfied Male Loyal Customer 14 Personal Travel Eco
32498 dissatisfied Male Loyal Customer 29 Personal Travel Eco
32546 dissatisfied Male Loyal Customer 31 Personal Travel Eco
32566 dissatisfied Female Loyal Customer 40 Personal Travel Eco
32592 satisfied Male Loyal Customer 7 Personal Travel Eco
32617 dissatisfied Male Loyal Customer 47 Personal Travel Eco
32619 dissatisfied Male Loyal Customer 57 Personal Travel Eco
32622 dissatisfied Male Loyal Customer 47 Personal Travel Eco Plus
32631 satisfied Male Loyal Customer 45 Personal Travel Eco
32676 dissatisfied Female Loyal Customer 49 Personal Travel Eco
32677 dissatisfied Female Loyal Customer 33 Personal Travel Eco
32681 dissatisfied Male Loyal Customer 48 Personal Travel Eco
32697 dissatisfied Female Loyal Customer 10 Personal Travel Eco
32702 dissatisfied Female Loyal Customer 52 Personal Travel Eco
32707 dissatisfied Male Loyal Customer 18 Personal Travel Eco
32723 satisfied Male Loyal Customer 37 Personal Travel Eco
32724 dissatisfied Male Loyal Customer 50 Personal Travel Eco
32754 dissatisfied Female Loyal Customer 28 Personal Travel Eco
32804 dissatisfied Male Loyal Customer 45 Personal Travel Eco
32814 satisfied Female Loyal Customer 59 Personal Travel Eco
32815 dissatisfied Male Loyal Customer 56 Personal Travel Eco
32817 dissatisfied Female Loyal Customer 17 Personal Travel Eco
32824 satisfied Female Loyal Customer 66 Personal Travel Eco Plus
32852 dissatisfied Female Loyal Customer 14 Personal Travel Eco
32870 dissatisfied Male Loyal Customer 18 Personal Travel Eco
32885 dissatisfied Male Loyal Customer 36 Personal Travel Eco
32899 dissatisfied Male Loyal Customer 26 Personal Travel Eco
32958 dissatisfied Male Loyal Customer 48 Personal Travel Eco
32983 dissatisfied Male Loyal Customer 15 Personal Travel Eco
32986 dissatisfied Female Loyal Customer 65 Personal Travel Eco
32987 dissatisfied Female Loyal Customer 47 Personal Travel Eco
33041 dissatisfied Male Loyal Customer 46 Personal Travel Eco
33056 dissatisfied Male Loyal Customer 30 Personal Travel Eco
33063 dissatisfied Male Loyal Customer 33 Personal Travel Eco
33079 dissatisfied Female Loyal Customer 18 Personal Travel Eco
33129 dissatisfied Female Loyal Customer 9 Personal Travel Eco Plus
33141 dissatisfied Female Loyal Customer 21 Personal Travel Eco
33143 dissatisfied Male Loyal Customer 18 Personal Travel Eco
33144 satisfied Male Loyal Customer 39 Personal Travel Eco
33152 dissatisfied Male Loyal Customer 21 Personal Travel Eco
33164 dissatisfied Female Loyal Customer 69 Personal Travel Business
33168 satisfied Female Loyal Customer 49 Personal Travel Eco
33172 dissatisfied Female Loyal Customer 57 Personal Travel Eco
33221 dissatisfied Female Loyal Customer 64 Personal Travel Eco
33229 satisfied Female Loyal Customer 44 Personal Travel Eco Plus
33232 dissatisfied Female Loyal Customer 61 Personal Travel Eco
33233 dissatisfied Female Loyal Customer 49 Personal Travel Eco Plus
33245 dissatisfied Male Loyal Customer 48 Personal Travel Business
33260 dissatisfied Female Loyal Customer 54 Personal Travel Eco
33262 dissatisfied Female Loyal Customer 28 Personal Travel Eco Plus
33263 dissatisfied Female Loyal Customer 27 Personal Travel Eco Plus
33268 dissatisfied Female Loyal Customer 33 Personal Travel Eco
33274 satisfied Male Loyal Customer 38 Personal Travel Eco
33275 dissatisfied Male Loyal Customer 61 Personal Travel Eco
33279 dissatisfied Male Loyal Customer 68 Personal Travel Eco
33283 dissatisfied Female Loyal Customer 31 Personal Travel Eco
33295 dissatisfied Female Loyal Customer 48 Personal Travel Eco
33300 satisfied Male Loyal Customer 62 Personal Travel Eco
33317 dissatisfied Female Loyal Customer 47 Personal Travel Eco
33330 dissatisfied Male Loyal Customer 70 Personal Travel Eco
33381 satisfied Female Loyal Customer 61 Personal Travel Eco
33396 dissatisfied Female Loyal Customer 45 Personal Travel Eco
33399 dissatisfied Male Loyal Customer 38 Personal Travel Eco Plus
33415 satisfied Male Loyal Customer 30 Personal Travel Eco
33416 dissatisfied Female Loyal Customer 8 Personal Travel Eco
33421 dissatisfied Female Loyal Customer 47 Personal Travel Eco
33437 dissatisfied Male Loyal Customer 12 Personal Travel Eco
33438 dissatisfied Female Loyal Customer 9 Personal Travel Eco
33444 dissatisfied Female Loyal Customer 25 Personal Travel Eco
33471 dissatisfied Male Loyal Customer 9 Personal Travel Eco
33482 dissatisfied Female Loyal Customer 36 Personal Travel Eco
33485 dissatisfied Female Loyal Customer 50 Personal Travel Eco
33515 dissatisfied Female Loyal Customer 63 Personal Travel Eco
33517 dissatisfied Female Loyal Customer 69 Personal Travel Business
33537 dissatisfied Male Loyal Customer 18 Personal Travel Eco
33539 dissatisfied Female Loyal Customer 37 Personal Travel Eco
33543 satisfied Male Loyal Customer 68 Personal Travel Eco
33559 dissatisfied Female Loyal Customer 27 Personal Travel Eco
33581 satisfied Male Loyal Customer 11 Personal Travel Eco
33595 satisfied Male Loyal Customer 66 Personal Travel Eco
33603 satisfied Male Loyal Customer 43 Personal Travel Eco
33608 dissatisfied Female Loyal Customer 45 Personal Travel Eco
33617 satisfied Female Loyal Customer 24 Personal Travel Eco
33663 dissatisfied Male Loyal Customer 61 Personal Travel Eco
33696 satisfied Male Loyal Customer 12 Personal Travel Business
33697 dissatisfied Female Loyal Customer 47 Personal Travel Eco
33705 dissatisfied Female Loyal Customer 22 Personal Travel Eco
33715 dissatisfied Male Loyal Customer 8 Personal Travel Business
33727 satisfied Male Loyal Customer 44 Personal Travel Eco
33731 dissatisfied Female Loyal Customer 42 Personal Travel Eco
33732 satisfied Female Loyal Customer 19 Personal Travel Eco Plus
33790 dissatisfied Female Loyal Customer 33 Personal Travel Eco
33807 dissatisfied Female Loyal Customer 31 Personal Travel Eco
33818 dissatisfied Female Loyal Customer 58 Personal Travel Eco
33828 dissatisfied Female Loyal Customer 41 Personal Travel Eco
33842 satisfied Male Loyal Customer 29 Personal Travel Eco
33855 dissatisfied Male Loyal Customer 10 Personal Travel Business
33856 dissatisfied Male Loyal Customer 68 Personal Travel Eco
33857 dissatisfied Female Loyal Customer 36 Personal Travel Eco
33868 dissatisfied Male Loyal Customer 46 Personal Travel Eco
33886 dissatisfied Female Loyal Customer 23 Personal Travel Eco
33906 dissatisfied Male Loyal Customer 22 Personal Travel Eco
33915 dissatisfied Male Loyal Customer 18 Personal Travel Eco
33918 satisfied Female Loyal Customer 39 Personal Travel Eco
33926 dissatisfied Male Loyal Customer 70 Personal Travel Eco
33934 dissatisfied Male Loyal Customer 46 Personal Travel Eco
33965 dissatisfied Male Loyal Customer 60 Personal Travel Eco
33976 satisfied Female Loyal Customer 40 Personal Travel Eco
34020 dissatisfied Male Loyal Customer 30 Personal Travel Eco
34023 dissatisfied Female Loyal Customer 60 Personal Travel Eco
34027 dissatisfied Female Loyal Customer 70 Personal Travel Eco
34031 dissatisfied Female Loyal Customer 35 Personal Travel Eco
34033 dissatisfied Male Loyal Customer 55 Personal Travel Eco
34050 dissatisfied Female Loyal Customer 8 Personal Travel Eco
34073 dissatisfied Male Loyal Customer 50 Personal Travel Business
34088 dissatisfied Male Loyal Customer 18 Personal Travel Eco
34090 satisfied Female Loyal Customer 36 Personal Travel Eco
34106 dissatisfied Male Loyal Customer 34 Personal Travel Eco
34112 dissatisfied Female Loyal Customer 52 Personal Travel Eco Plus
34117 satisfied Male Loyal Customer 10 Personal Travel Eco
34138 satisfied Male Loyal Customer 50 Personal Travel Eco
34160 satisfied Female Loyal Customer 19 Personal Travel Eco
34164 satisfied Male Loyal Customer 50 Personal Travel Business
34168 satisfied Female Loyal Customer 29 Personal Travel Eco
34169 dissatisfied Female Loyal Customer 7 Personal Travel Eco
34178 dissatisfied Male Loyal Customer 38 Personal Travel Eco
34186 satisfied Female Loyal Customer 30 Personal Travel Eco Plus
34192 satisfied Male Loyal Customer 53 Personal Travel Eco Plus
34221 satisfied Female Loyal Customer 51 Personal Travel Eco
34223 satisfied Male Loyal Customer 20 Personal Travel Business
34224 satisfied Female Loyal Customer 56 Personal Travel Eco
34231 dissatisfied Female Loyal Customer 32 Personal Travel Eco
34243 dissatisfied Male Loyal Customer 50 Personal Travel Eco
34256 dissatisfied Male Loyal Customer 40 Personal Travel Eco
34288 satisfied Male Loyal Customer 64 Personal Travel Eco
34298 dissatisfied Male Loyal Customer 66 Personal Travel Eco
34339 satisfied Female Loyal Customer 47 Personal Travel Eco
34344 dissatisfied Male Loyal Customer 51 Personal Travel Business
34354 dissatisfied Male Loyal Customer 69 Personal Travel Eco
34361 satisfied Male Loyal Customer 49 Personal Travel Eco
34370 satisfied Female Loyal Customer 41 Personal Travel Eco
34384 dissatisfied Male Loyal Customer 7 Personal Travel Eco Plus
34385 dissatisfied Male Loyal Customer 39 Personal Travel Eco
34406 satisfied Male Loyal Customer 45 Personal Travel Eco
34444 dissatisfied Female Loyal Customer 30 Personal Travel Business
34454 dissatisfied Male Loyal Customer 48 Personal Travel Eco
34473 dissatisfied Male Loyal Customer 68 Personal Travel Eco
34480 satisfied Female Loyal Customer 41 Personal Travel Eco
34501 dissatisfied Female Loyal Customer 14 Personal Travel Eco Plus
34552 dissatisfied Female Loyal Customer 24 Personal Travel Eco
34560 dissatisfied Male Loyal Customer 38 Personal Travel Eco
34564 dissatisfied Female Loyal Customer 41 Personal Travel Eco
34589 satisfied Female Loyal Customer 45 Personal Travel Eco
34607 satisfied Female Loyal Customer 68 Personal Travel Eco
34614 satisfied Male Loyal Customer 57 Personal Travel Business
34635 dissatisfied Female Loyal Customer 28 Personal Travel Eco
34636 dissatisfied Female Loyal Customer 10 Personal Travel Eco Plus
34638 satisfied Male Loyal Customer 33 Personal Travel Eco
34648 dissatisfied Female Loyal Customer 22 Personal Travel Eco
34653 dissatisfied Female Loyal Customer 15 Personal Travel Eco
34689 satisfied Male Loyal Customer 64 Personal Travel Eco
34696 dissatisfied Female Loyal Customer 42 Personal Travel Eco
34714 dissatisfied Female Loyal Customer 64 Personal Travel Eco
34719 dissatisfied Male Loyal Customer 34 Personal Travel Eco
34729 dissatisfied Female Loyal Customer 22 Personal Travel Eco
34732 dissatisfied Male Loyal Customer 59 Personal Travel Eco
34734 satisfied Female Loyal Customer 29 Personal Travel Eco
34738 dissatisfied Female Loyal Customer 27 Personal Travel Eco
34751 dissatisfied Female Loyal Customer 18 Personal Travel Eco
34752 satisfied Female Loyal Customer 27 Personal Travel Eco
34789 dissatisfied Female Loyal Customer 63 Personal Travel Eco
34795 dissatisfied Male Loyal Customer 27 Personal Travel Eco
34856 dissatisfied Female Loyal Customer 36 Personal Travel Eco
34867 dissatisfied Female Loyal Customer 46 Personal Travel Eco
34906 dissatisfied Female Loyal Customer 31 Personal Travel Eco
34920 dissatisfied Female Loyal Customer 29 Personal Travel Eco
34924 dissatisfied Male Loyal Customer 43 Personal Travel Eco
34931 dissatisfied Female Loyal Customer 43 Personal Travel Eco
34933 dissatisfied Female Loyal Customer 53 Personal Travel Eco
34942 satisfied Female Loyal Customer 26 Personal Travel Eco
34950 satisfied Female Loyal Customer 67 Personal Travel Eco
34956 satisfied Male Loyal Customer 54 Personal Travel Eco
35030 dissatisfied Male Loyal Customer 11 Personal Travel Eco
35061 satisfied Female Loyal Customer 23 Personal Travel Eco
35065 satisfied Female Loyal Customer 47 Personal Travel Business
35076 satisfied Female Loyal Customer 24 Personal Travel Eco
35098 dissatisfied Male Loyal Customer 52 Personal Travel Eco
35099 dissatisfied Male Loyal Customer 38 Personal Travel Eco
35109 dissatisfied Female Loyal Customer 51 Personal Travel Eco Plus
35137 satisfied Male Loyal Customer 36 Personal Travel Eco
35144 dissatisfied Female Loyal Customer 45 Personal Travel Eco
35145 dissatisfied Female Loyal Customer 54 Personal Travel Eco Plus
35160 dissatisfied Female Loyal Customer 56 Personal Travel Eco Plus
35191 dissatisfied Male Loyal Customer 19 Personal Travel Eco
35197 dissatisfied Male Loyal Customer 41 Personal Travel Eco
35236 dissatisfied Male Loyal Customer 57 Personal Travel Eco
35266 dissatisfied Male Loyal Customer 20 Personal Travel Eco
35272 dissatisfied Male Loyal Customer 29 Personal Travel Eco
35279 dissatisfied Male Loyal Customer 57 Personal Travel Eco
35282 dissatisfied Male Loyal Customer 19 Personal Travel Eco
35284 satisfied Female Loyal Customer 23 Personal Travel Eco
35304 dissatisfied Female Loyal Customer 48 Personal Travel Eco
35330 dissatisfied Male Loyal Customer 65 Personal Travel Eco
35342 dissatisfied Female Loyal Customer 34 Personal Travel Eco
35349 satisfied Female Loyal Customer 20 Personal Travel Eco
35354 dissatisfied Male Loyal Customer 61 Personal Travel Eco
35358 dissatisfied Male Loyal Customer 46 Personal Travel Eco
35428 dissatisfied Female Loyal Customer 32 Personal Travel Eco
35432 dissatisfied Female Loyal Customer 21 Personal Travel Eco
35440 dissatisfied Male Loyal Customer 40 Personal Travel Eco Plus
35446 satisfied Male Loyal Customer 41 Personal Travel Eco
35447 dissatisfied Male Loyal Customer 19 Personal Travel Eco
35448 satisfied Female Loyal Customer 47 Personal Travel Eco
35462 dissatisfied Female Loyal Customer 56 Personal Travel Eco
35476 dissatisfied Female Loyal Customer 25 Personal Travel Eco
35484 satisfied Female Loyal Customer 46 Personal Travel Eco
35500 dissatisfied Male Loyal Customer 49 Personal Travel Eco
35504 dissatisfied Female Loyal Customer 18 Personal Travel Eco
35527 satisfied Female Loyal Customer 67 Personal Travel Eco
35529 satisfied Male Loyal Customer 30 Personal Travel Eco
35532 dissatisfied Male Loyal Customer 14 Personal Travel Eco
35546 dissatisfied Female Loyal Customer 27 Personal Travel Eco
35556 dissatisfied Female Loyal Customer 41 Personal Travel Eco
35559 satisfied Male Loyal Customer 44 Personal Travel Eco
35582 dissatisfied Male Loyal Customer 26 Personal Travel Eco
35589 satisfied Male Loyal Customer 40 Personal Travel Eco
35594 dissatisfied Male Loyal Customer 20 Personal Travel Eco
35604 dissatisfied Male Loyal Customer 20 Personal Travel Eco
35613 dissatisfied Male Loyal Customer 18 Personal Travel Eco
35615 dissatisfied Male Loyal Customer 32 Personal Travel Eco
35621 dissatisfied Male Loyal Customer 28 Personal Travel Eco
35629 dissatisfied Male Loyal Customer 14 Personal Travel Eco
35634 dissatisfied Male Loyal Customer 11 Personal Travel Eco
35647 dissatisfied Male Loyal Customer 34 Personal Travel Eco
35648 satisfied Male Loyal Customer 16 Personal Travel Eco
35662 dissatisfied Male Loyal Customer 41 Personal Travel Eco Plus
35674 satisfied Male Loyal Customer 35 Personal Travel Business
35676 dissatisfied Male Loyal Customer 11 Personal Travel Eco
35679 dissatisfied Male Loyal Customer 11 Personal Travel Eco
35732 dissatisfied Male Loyal Customer 8 Personal Travel Eco
35735 dissatisfied Female Loyal Customer 26 Personal Travel Eco
35739 dissatisfied Male Loyal Customer 45 Personal Travel Eco Plus
35752 dissatisfied Female Loyal Customer 19 Personal Travel Eco
35753 satisfied Female Loyal Customer 10 Personal Travel Eco
35772 satisfied Female Loyal Customer 26 Personal Travel Eco
35778 satisfied Female Loyal Customer 17 Personal Travel Eco
35784 dissatisfied Female Loyal Customer 56 Personal Travel Eco
35802 dissatisfied Male Loyal Customer 30 Personal Travel Eco
35808 dissatisfied Male Loyal Customer 53 Personal Travel Eco
35822 dissatisfied Female Loyal Customer 33 Personal Travel Eco
35853 satisfied Female Loyal Customer 30 Personal Travel Eco
35858 dissatisfied Male Loyal Customer 9 Personal Travel Eco Plus
35879 satisfied Male Loyal Customer 14 Personal Travel Eco
35881 dissatisfied Male Loyal Customer 22 Personal Travel Eco
35882 satisfied Male Loyal Customer 68 Personal Travel Eco
35895 dissatisfied Male Loyal Customer 59 Personal Travel Eco
35905 satisfied Male Loyal Customer 26 Personal Travel Eco
35934 dissatisfied Male Loyal Customer 57 Personal Travel Eco
35938 dissatisfied Male Loyal Customer 52 Personal Travel Eco
35943 dissatisfied Male Loyal Customer 49 Personal Travel Eco
36000 dissatisfied Male Loyal Customer 8 Personal Travel Eco
36012 dissatisfied Female Loyal Customer 69 Personal Travel Eco
36020 dissatisfied Male Loyal Customer 69 Personal Travel Eco
36038 dissatisfied Male Loyal Customer 36 Personal Travel Business
36082 satisfied Female Loyal Customer 9 Personal Travel Eco
36093 dissatisfied Female Loyal Customer 65 Personal Travel Eco Plus
36108 satisfied Male Loyal Customer 28 Personal Travel Eco
36113 satisfied Male Loyal Customer 54 Personal Travel Eco Plus
36122 satisfied Female Loyal Customer 34 Personal Travel Eco
36133 dissatisfied Female Loyal Customer 51 Personal Travel Eco
36157 dissatisfied Male Loyal Customer 39 Personal Travel Eco
36158 dissatisfied Male Loyal Customer 30 Personal Travel Eco
36164 dissatisfied Female Loyal Customer 11 Personal Travel Eco
36169 dissatisfied Male Loyal Customer 57 Personal Travel Business
36176 dissatisfied Male Loyal Customer 7 Personal Travel Eco
36185 dissatisfied Female Loyal Customer 30 Personal Travel Eco
36195 dissatisfied Female Loyal Customer 19 Personal Travel Eco
36201 satisfied Male Loyal Customer 9 Personal Travel Eco
36228 dissatisfied Male Loyal Customer 9 Personal Travel Eco
36244 dissatisfied Male Loyal Customer 45 Personal Travel Eco
36261 satisfied Female Loyal Customer 41 Personal Travel Business
36296 dissatisfied Female Loyal Customer 22 Personal Travel Eco
36355 satisfied Male Loyal Customer 70 Personal Travel Business
36365 dissatisfied Male Loyal Customer 40 Personal Travel Eco
36374 satisfied Male Loyal Customer 29 Personal Travel Eco
36397 dissatisfied Male Loyal Customer 41 Personal Travel Eco
36404 dissatisfied Female Loyal Customer 15 Personal Travel Eco
36414 satisfied Female Loyal Customer 63 Personal Travel Eco
36422 satisfied Male Loyal Customer 62 Personal Travel Eco
36425 dissatisfied Female Loyal Customer 15 Personal Travel Eco
36434 dissatisfied Male Loyal Customer 49 Personal Travel Eco
36440 satisfied Male Loyal Customer 68 Personal Travel Eco
36461 dissatisfied Male Loyal Customer 39 Personal Travel Eco
36462 dissatisfied Female Loyal Customer 7 Personal Travel Eco
36477 dissatisfied Female Loyal Customer 26 Personal Travel Eco
36491 dissatisfied Female Loyal Customer 62 Personal Travel Eco
36494 satisfied Female Loyal Customer 48 Personal Travel Eco
36502 dissatisfied Male Loyal Customer 68 Personal Travel Eco
36507 dissatisfied Male Loyal Customer 14 Personal Travel Eco
36542 dissatisfied Female Loyal Customer 22 Personal Travel Eco Plus
36557 satisfied Male Loyal Customer 67 Personal Travel Eco
36560 satisfied Male Loyal Customer 53 Personal Travel Eco
36564 dissatisfied Female Loyal Customer 68 Personal Travel Eco
36583 dissatisfied Female Loyal Customer 45 Personal Travel Eco
36605 dissatisfied Male Loyal Customer 10 Personal Travel Eco
36608 dissatisfied Male Loyal Customer 33 Personal Travel Eco
36609 dissatisfied Female Loyal Customer 25 Personal Travel Eco
36610 dissatisfied Male Loyal Customer 60 Personal Travel Eco
36616 dissatisfied Female Loyal Customer 33 Personal Travel Eco
36617 dissatisfied Female Loyal Customer 30 Personal Travel Eco
36618 dissatisfied Male Loyal Customer 11 Personal Travel Eco
36622 dissatisfied Male Loyal Customer 15 Personal Travel Eco
36628 dissatisfied Female Loyal Customer 67 Personal Travel Eco
36636 dissatisfied Female Loyal Customer 29 Personal Travel Eco
36644 dissatisfied Female Loyal Customer 10 Personal Travel Business
36677 satisfied Female Loyal Customer 19 Personal Travel Eco
36692 dissatisfied Male Loyal Customer 53 Personal Travel Eco
36712 satisfied Male Loyal Customer 7 Personal Travel Eco
36720 dissatisfied Male Loyal Customer 30 Personal Travel Eco
36724 dissatisfied Male Loyal Customer 23 Personal Travel Eco
36727 dissatisfied Female Loyal Customer 63 Personal Travel Eco
36765 dissatisfied Female Loyal Customer 15 Personal Travel Eco
36782 dissatisfied Female Loyal Customer 23 Personal Travel Eco
36783 satisfied Male Loyal Customer 24 Personal Travel Eco
36791 dissatisfied Female Loyal Customer 66 Personal Travel Eco
36801 dissatisfied Female Loyal Customer 44 Personal Travel Eco
36803 dissatisfied Female Loyal Customer 26 Personal Travel Eco
36848 satisfied Female Loyal Customer 69 Personal Travel Eco Plus
36858 dissatisfied Male Loyal Customer 33 Personal Travel Eco
36860 dissatisfied Male Loyal Customer 34 Personal Travel Eco
36865 dissatisfied Male Loyal Customer 61 Personal Travel Eco
36869 satisfied Male Loyal Customer 70 Personal Travel Eco
36871 satisfied Female Loyal Customer 64 Personal Travel Eco
36872 satisfied Female Loyal Customer 12 Personal Travel Eco
36904 dissatisfied Male Loyal Customer 36 Personal Travel Eco Plus
36932 dissatisfied Male Loyal Customer 23 Personal Travel Eco
36935 dissatisfied Female Loyal Customer 7 Personal Travel Eco
36951 satisfied Female Loyal Customer 40 Personal Travel Eco
36957 dissatisfied Male Loyal Customer 61 Personal Travel Eco Plus
37002 dissatisfied Male Loyal Customer 19 Personal Travel Eco
37030 satisfied Male Loyal Customer 63 Personal Travel Eco
37042 dissatisfied Female Loyal Customer 23 Personal Travel Eco
37063 satisfied Male Loyal Customer 39 Personal Travel Eco
37071 dissatisfied Female Loyal Customer 28 Personal Travel Eco
37073 dissatisfied Male Loyal Customer 43 Personal Travel Eco
37081 dissatisfied Female Loyal Customer 46 Personal Travel Eco
37089 satisfied Female Loyal Customer 65 Personal Travel Eco
37177 satisfied Male Loyal Customer 14 Personal Travel Eco
37179 dissatisfied Male Loyal Customer 12 Personal Travel Eco
37186 satisfied Female Loyal Customer 23 Personal Travel Eco
37191 satisfied Female Loyal Customer 59 Personal Travel Business
37199 dissatisfied Female Loyal Customer 37 Personal Travel Eco
37221 dissatisfied Male Loyal Customer 9 Personal Travel Business
37250 dissatisfied Female Loyal Customer 12 Personal Travel Eco
37257 dissatisfied Male Loyal Customer 7 Personal Travel Eco
37270 satisfied Male Loyal Customer 46 Personal Travel Eco
37273 dissatisfied Female Loyal Customer 66 Personal Travel Business
37310 dissatisfied Male Loyal Customer 29 Personal Travel Eco Plus
37315 dissatisfied Male Loyal Customer 17 Personal Travel Eco
37326 satisfied Male Loyal Customer 53 Personal Travel Eco
37351 dissatisfied Male Loyal Customer 23 Personal Travel Business
37359 satisfied Female Loyal Customer 69 Personal Travel Eco
37368 satisfied Male Loyal Customer 31 Personal Travel Eco
37372 dissatisfied Male Loyal Customer 29 Personal Travel Eco
37388 dissatisfied Male Loyal Customer 70 Personal Travel Eco
37400 dissatisfied Female Loyal Customer 62 Personal Travel Eco
37404 dissatisfied Male Loyal Customer 10 Personal Travel Eco
37420 dissatisfied Female Loyal Customer 33 Personal Travel Eco
37456 dissatisfied Female Loyal Customer 14 Personal Travel Eco Plus
37460 dissatisfied Male Loyal Customer 43 Personal Travel Eco
37462 dissatisfied Male Loyal Customer 18 Personal Travel Eco
37467 dissatisfied Male Loyal Customer 53 Personal Travel Eco
37484 dissatisfied Male Loyal Customer 28 Personal Travel Eco
37503 dissatisfied Male Loyal Customer 66 Personal Travel Eco
37513 satisfied Male Loyal Customer 52 Personal Travel Business
37516 dissatisfied Male Loyal Customer 12 Personal Travel Eco
37525 satisfied Male Loyal Customer 9 Personal Travel Eco
37529 dissatisfied Female Loyal Customer 66 Personal Travel Eco
37544 satisfied Male Loyal Customer 39 Personal Travel Eco
37559 dissatisfied Female Loyal Customer 57 Personal Travel Eco
37573 dissatisfied Male Loyal Customer 59 Personal Travel Eco
37631 satisfied Male Loyal Customer 70 Personal Travel Eco
37663 satisfied Male Loyal Customer 55 Personal Travel Business
37686 dissatisfied Female Loyal Customer 31 Personal Travel Eco Plus
37692 dissatisfied Male Loyal Customer 58 Personal Travel Eco
37705 dissatisfied Male Loyal Customer 65 Personal Travel Eco
37724 dissatisfied Female Loyal Customer 69 Personal Travel Eco
37742 dissatisfied Male Loyal Customer 65 Personal Travel Eco Plus
37748 dissatisfied Female Loyal Customer 44 Personal Travel Eco
37760 dissatisfied Male Loyal Customer 34 Personal Travel Eco
37767 dissatisfied Male Loyal Customer 61 Personal Travel Eco
37779 satisfied Male Loyal Customer 52 Personal Travel Eco
37796 dissatisfied Male Loyal Customer 32 Personal Travel Business
37799 dissatisfied Male Loyal Customer 27 Personal Travel Eco
37841 dissatisfied Male Loyal Customer 67 Personal Travel Eco
37853 dissatisfied Male Loyal Customer 55 Personal Travel Eco
37862 dissatisfied Male Loyal Customer 46 Personal Travel Eco Plus
37872 dissatisfied Female Loyal Customer 32 Personal Travel Eco
37882 dissatisfied Female Loyal Customer 41 Personal Travel Eco
37885 dissatisfied Female Loyal Customer 39 Personal Travel Eco Plus
37907 satisfied Female Loyal Customer 27 Personal Travel Eco
37911 dissatisfied Female Loyal Customer 23 Personal Travel Eco
37913 satisfied Male Loyal Customer 17 Personal Travel Eco
37931 dissatisfied Male Loyal Customer 50 Personal Travel Eco
37932 dissatisfied Male Loyal Customer 20 Personal Travel Eco
37945 dissatisfied Male Loyal Customer 28 Personal Travel Eco Plus
37980 satisfied Female Loyal Customer 60 Personal Travel Eco
37989 dissatisfied Female Loyal Customer 50 Personal Travel Eco
37990 dissatisfied Female Loyal Customer 57 Personal Travel Eco Plus
38005 dissatisfied Male Loyal Customer 45 Personal Travel Eco
38009 dissatisfied Male Loyal Customer 50 Personal Travel Eco
38020 dissatisfied Male Loyal Customer 61 Personal Travel Eco
38027 satisfied Female Loyal Customer 11 Personal Travel Eco Plus
38033 dissatisfied Female Loyal Customer 35 Personal Travel Eco
38038 satisfied Male Loyal Customer 64 Personal Travel Eco
38065 satisfied Female Loyal Customer 22 Personal Travel Eco
38079 dissatisfied Female Loyal Customer 36 Personal Travel Eco
38091 dissatisfied Male Loyal Customer 40 Personal Travel Eco
38097 satisfied Male Loyal Customer 28 Personal Travel Eco
38105 dissatisfied Male Loyal Customer 56 Personal Travel Eco
38111 dissatisfied Male Loyal Customer 9 Personal Travel Eco
38126 dissatisfied Male Loyal Customer 47 Personal Travel Eco
38144 satisfied Male Loyal Customer 20 Personal Travel Eco
38166 dissatisfied Female Loyal Customer 37 Personal Travel Eco
38180 dissatisfied Male Loyal Customer 22 Personal Travel Eco
38185 dissatisfied Female Loyal Customer 46 Personal Travel Eco
38190 dissatisfied Female Loyal Customer 23 Personal Travel Eco
38193 dissatisfied Male Loyal Customer 38 Personal Travel Eco Plus
38207 satisfied Male Loyal Customer 41 Personal Travel Eco
38213 dissatisfied Male Loyal Customer 7 Personal Travel Eco
38217 dissatisfied Female Loyal Customer 52 Personal Travel Eco
38235 satisfied Female Loyal Customer 52 Personal Travel Eco
38280 dissatisfied Male Loyal Customer 56 Personal Travel Eco
38297 dissatisfied Male Loyal Customer 44 Personal Travel Business
38299 satisfied Male Loyal Customer 12 Personal Travel Eco
38307 dissatisfied Male Loyal Customer 13 Personal Travel Eco
38346 dissatisfied Male Loyal Customer 35 Personal Travel Eco
38358 dissatisfied Male Loyal Customer 18 Personal Travel Eco
38367 dissatisfied Female Loyal Customer 66 Personal Travel Eco
38432 satisfied Male Loyal Customer 37 Personal Travel Eco
38444 satisfied Male Loyal Customer 53 Personal Travel Eco
38453 satisfied Male Loyal Customer 66 Personal Travel Eco Plus
38457 satisfied Female Loyal Customer 17 Personal Travel Eco Plus
38529 satisfied Female Loyal Customer 35 Personal Travel Business
38549 satisfied Female Loyal Customer 32 Personal Travel Eco
38556 satisfied Female Loyal Customer 7 Personal Travel Eco
38571 satisfied Male Loyal Customer 43 Personal Travel Business
38591 satisfied Female Loyal Customer 17 Personal Travel Eco
38595 satisfied Female Loyal Customer 68 Personal Travel Eco
38618 satisfied Female Loyal Customer 19 Personal Travel Eco
38666 satisfied Female Loyal Customer 27 Personal Travel Eco
38681 satisfied Female Loyal Customer 24 Personal Travel Eco Plus
38682 satisfied Female Loyal Customer 51 Personal Travel Business
38712 satisfied Male Loyal Customer 56 Personal Travel Eco
38720 satisfied Female Loyal Customer 24 Personal Travel Eco Plus
38737 satisfied Female Loyal Customer 54 Personal Travel Eco
38738 satisfied Female Loyal Customer 60 Personal Travel Eco
38744 satisfied Female Loyal Customer 63 Personal Travel Eco
38746 satisfied Female Loyal Customer 70 Personal Travel Eco
38765 satisfied Male Loyal Customer 10 Personal Travel Eco
38768 satisfied Female Loyal Customer 44 Personal Travel Business
38774 satisfied Female Loyal Customer 29 Personal Travel Eco Plus
38778 satisfied Female Loyal Customer 63 Personal Travel Eco
38781 satisfied Female Loyal Customer 54 Personal Travel Eco Plus
38790 satisfied Male Loyal Customer 61 Personal Travel Eco
38803 satisfied Female Loyal Customer 21 Personal Travel Eco
38804 satisfied Female Loyal Customer 58 Personal Travel Eco
38806 satisfied Male Loyal Customer 60 Personal Travel Eco Plus
38825 satisfied Male Loyal Customer 57 Personal Travel Eco
38833 satisfied Male Loyal Customer 50 Personal Travel Eco
38837 satisfied Female Loyal Customer 53 Personal Travel Eco
38877 satisfied Female Loyal Customer 15 Personal Travel Eco
38883 satisfied Female Loyal Customer 9 Personal Travel Eco
38894 satisfied Female Loyal Customer 53 Personal Travel Eco
38895 satisfied Male Loyal Customer 31 Personal Travel Eco
38900 satisfied Female Loyal Customer 8 Personal Travel Eco
38901 satisfied Male Loyal Customer 35 Personal Travel Eco
38923 satisfied Female Loyal Customer 10 Personal Travel Eco
38931 satisfied Male Loyal Customer 7 Personal Travel Eco
38939 satisfied Male Loyal Customer 14 Personal Travel Eco
38942 satisfied Male Loyal Customer 64 Personal Travel Eco
38983 satisfied Female Loyal Customer 25 Personal Travel Eco Plus
39006 satisfied Male Loyal Customer 41 Personal Travel Eco Plus
39025 satisfied Male Loyal Customer 53 Personal Travel Eco
39039 satisfied Female Loyal Customer 32 Personal Travel Eco
39062 satisfied Male Loyal Customer 59 Personal Travel Eco
39067 satisfied Male Loyal Customer 17 Personal Travel Eco
39071 satisfied Male Loyal Customer 46 Personal Travel Eco
39081 satisfied Male Loyal Customer 20 Personal Travel Eco
39119 satisfied Female Loyal Customer 23 Personal Travel Eco Plus
39124 satisfied Female Loyal Customer 41 Personal Travel Eco
39163 satisfied Male Loyal Customer 9 Personal Travel Eco
39176 satisfied Female Loyal Customer 34 Personal Travel Eco Plus
39182 satisfied Female Loyal Customer 33 Personal Travel Eco
39183 satisfied Male Loyal Customer 15 Personal Travel Eco
39184 satisfied Female Loyal Customer 48 Personal Travel Eco
39186 satisfied Female Loyal Customer 59 Personal Travel Eco
39189 satisfied Female Loyal Customer 28 Personal Travel Eco
39191 satisfied Female Loyal Customer 68 Personal Travel Eco Plus
39205 satisfied Female Loyal Customer 36 Personal Travel Eco
39207 satisfied Female Loyal Customer 10 Personal Travel Eco
39214 satisfied Female Loyal Customer 31 Personal Travel Eco
39217 satisfied Female Loyal Customer 12 Personal Travel Eco
39218 satisfied Male Loyal Customer 32 Personal Travel Eco
39223 satisfied Female Loyal Customer 52 Personal Travel Business
39243 satisfied Male Loyal Customer 11 Personal Travel Eco
39252 satisfied Female Loyal Customer 57 Personal Travel Eco
39259 satisfied Male Loyal Customer 59 Personal Travel Eco
39270 satisfied Female Loyal Customer 63 Personal Travel Eco
39296 satisfied Female Loyal Customer 19 Personal Travel Eco
39301 satisfied Female Loyal Customer 55 Personal Travel Eco
39316 satisfied Male Loyal Customer 29 Personal Travel Eco
39331 satisfied Male Loyal Customer 33 Personal Travel Eco
39333 satisfied Male Loyal Customer 64 Personal Travel Eco
39356 satisfied Male Loyal Customer 27 Personal Travel Eco
39370 satisfied Female Loyal Customer 18 Personal Travel Business
39394 satisfied Female Loyal Customer 33 Personal Travel Eco Plus
39419 satisfied Male Loyal Customer 44 Personal Travel Eco
39427 satisfied Female Loyal Customer 13 Personal Travel Eco
39429 satisfied Female Loyal Customer 23 Personal Travel Eco
39464 satisfied Female Loyal Customer 20 Personal Travel Eco
39489 satisfied Female Loyal Customer 51 Personal Travel Eco
39505 satisfied Female Loyal Customer 13 Personal Travel Eco
39506 satisfied Male Loyal Customer 24 Personal Travel Eco
39512 satisfied Female Loyal Customer 14 Personal Travel Business
39518 satisfied Male Loyal Customer 8 Personal Travel Eco Plus
39564 satisfied Male Loyal Customer 48 Personal Travel Eco
39575 satisfied Female Loyal Customer 58 Personal Travel Eco
39577 satisfied Female Loyal Customer 49 Personal Travel Eco
39598 satisfied Female Loyal Customer 66 Personal Travel Eco Plus
39605 satisfied Female Loyal Customer 41 Personal Travel Eco
39610 satisfied Female Loyal Customer 14 Personal Travel Business
39618 satisfied Male Loyal Customer 68 Personal Travel Eco Plus
39631 satisfied Male Loyal Customer 64 Personal Travel Business
39639 satisfied Male Loyal Customer 42 Personal Travel Eco
39642 satisfied Male Loyal Customer 23 Personal Travel Eco
39662 satisfied Female Loyal Customer 46 Personal Travel Business
39667 satisfied Female Loyal Customer 16 Personal Travel Eco
39691 satisfied Male Loyal Customer 47 Personal Travel Eco
39698 satisfied Female Loyal Customer 14 Personal Travel Eco
39708 satisfied Female Loyal Customer 37 Personal Travel Eco
39724 satisfied Female Loyal Customer 40 Personal Travel Eco
39733 satisfied Female Loyal Customer 57 Personal Travel Eco
39747 satisfied Male Loyal Customer 41 Personal Travel Business
39749 satisfied Female Loyal Customer 13 Personal Travel Eco
39754 satisfied Male Loyal Customer 15 Personal Travel Eco Plus
39759 satisfied Female Loyal Customer 53 Personal Travel Eco
39769 satisfied Female Loyal Customer 12 Personal Travel Eco
39771 satisfied Male Loyal Customer 7 Personal Travel Eco Plus
39793 satisfied Male Loyal Customer 36 Personal Travel Eco
39795 satisfied Male Loyal Customer 34 Personal Travel Eco
39797 satisfied Female Loyal Customer 11 Personal Travel Eco
39798 satisfied Female Loyal Customer 15 Personal Travel Eco
39843 satisfied Female Loyal Customer 27 Personal Travel Eco
39853 satisfied Male Loyal Customer 42 Personal Travel Eco
39867 satisfied Male Loyal Customer 28 Personal Travel Eco
39894 satisfied Male Loyal Customer 44 Personal Travel Eco
39909 satisfied Male Loyal Customer 44 Personal Travel Eco
39931 satisfied Female Loyal Customer 40 Personal Travel Eco
39935 satisfied Male Loyal Customer 38 Personal Travel Eco
39977 satisfied Male Loyal Customer 61 Personal Travel Eco
40018 satisfied Female disloyal Customer 22 Business travel Eco
40031 satisfied Male disloyal Customer 26 Business travel Business
40032 satisfied Male disloyal Customer 26 Business travel Business
40055 satisfied Female disloyal Customer 24 Business travel Business
40071 satisfied Male disloyal Customer 21 Business travel Eco
40073 satisfied Male disloyal Customer 22 Business travel Business
40077 satisfied Female disloyal Customer 22 Business travel Business
40140 satisfied Male disloyal Customer 27 Business travel Business
40152 satisfied Female disloyal Customer 26 Business travel Business
40153 satisfied Female disloyal Customer 26 Business travel Business
40156 satisfied Male disloyal Customer 24 Business travel Eco
40164 satisfied Female disloyal Customer 25 Business travel Business
40193 satisfied Male disloyal Customer 22 Business travel Business
40208 satisfied Female disloyal Customer 18 Business travel Business
40217 satisfied Male disloyal Customer 23 Business travel Eco
40219 satisfied Female disloyal Customer 24 Business travel Eco
40225 satisfied Male disloyal Customer 29 Business travel Business
40236 satisfied Male disloyal Customer 28 Business travel Business
40241 satisfied Male disloyal Customer 22 Business travel Eco
40257 satisfied Male disloyal Customer 27 Business travel Business
40279 satisfied Female disloyal Customer 25 Business travel Business
40285 satisfied Female disloyal Customer 22 Business travel Eco
40325 satisfied Female disloyal Customer 48 Business travel Eco
40370 satisfied Female disloyal Customer 27 Business travel Business
40395 satisfied Male disloyal Customer 25 Business travel Business
40409 satisfied Female disloyal Customer 24 Business travel Business
40415 satisfied Female disloyal Customer 24 Business travel Business
40416 satisfied Male disloyal Customer 24 Business travel Business
40434 satisfied Male disloyal Customer 26 Business travel Eco
40437 satisfied Female disloyal Customer 22 Business travel Business
40441 satisfied Male disloyal Customer 22 Business travel Eco
40447 satisfied Male disloyal Customer 21 Business travel Business
40483 satisfied Male disloyal Customer 27 Business travel Business
40487 satisfied Male disloyal Customer 25 Business travel Business
40496 satisfied Female disloyal Customer 24 Business travel Eco
40523 satisfied Male disloyal Customer 15 Business travel Business
40524 satisfied Female disloyal Customer 14 Business travel Business
40548 satisfied Male disloyal Customer 19 Business travel Business
40550 satisfied Female disloyal Customer 21 Business travel Eco
40557 satisfied Female disloyal Customer 25 Business travel Business
40559 satisfied Female disloyal Customer 20 Business travel Eco
40565 satisfied Male disloyal Customer 25 Business travel Business
40585 satisfied Female disloyal Customer 22 Business travel Business
40588 satisfied Female disloyal Customer 38 Business travel Eco
40629 satisfied Male disloyal Customer 25 Business travel Business
40639 satisfied Female disloyal Customer 25 Business travel Business
40664 satisfied Male disloyal Customer 24 Business travel Business
40719 satisfied Female disloyal Customer 18 Business travel Business
40728 satisfied Male disloyal Customer 24 Business travel Business
40729 satisfied Male disloyal Customer 24 Business travel Business
40749 satisfied Female disloyal Customer 22 Business travel Business
40761 satisfied Female disloyal Customer 23 Business travel Business
40762 satisfied Male disloyal Customer 23 Business travel Eco
40774 satisfied Female disloyal Customer 20 Business travel Business
40780 satisfied Female disloyal Customer 24 Business travel Eco
40790 satisfied Female disloyal Customer 22 Business travel Business
40810 satisfied Male disloyal Customer 23 Business travel Eco
40844 satisfied Female disloyal Customer 46 Business travel Eco
40851 satisfied Male disloyal Customer 22 Business travel Eco
40895 dissatisfied Female disloyal Customer 34 Business travel Business
40899 dissatisfied Male disloyal Customer 28 Business travel Business
40926 dissatisfied Male disloyal Customer 22 Business travel Eco
40934 dissatisfied Female disloyal Customer 15 Business travel Eco
40948 dissatisfied Male disloyal Customer 54 Business travel Eco
40978 dissatisfied Female disloyal Customer 44 Business travel Business
40999 dissatisfied Male disloyal Customer 52 Business travel Eco
41003 dissatisfied Female disloyal Customer 19 Business travel Eco
41018 dissatisfied Male disloyal Customer 39 Business travel Business
41024 dissatisfied Female disloyal Customer 30 Business travel Business
41040 dissatisfied Male disloyal Customer 25 Business travel Business
41050 dissatisfied Male disloyal Customer 22 Business travel Eco
41051 dissatisfied Male disloyal Customer 38 Business travel Eco
41054 dissatisfied Male disloyal Customer 38 Business travel Eco
41061 dissatisfied Male disloyal Customer 36 Business travel Eco
41073 dissatisfied Female disloyal Customer 43 Business travel Eco
41074 dissatisfied Male disloyal Customer 24 Business travel Eco
41087 dissatisfied Female disloyal Customer 26 Business travel Business
41098 dissatisfied Female disloyal Customer 34 Business travel Eco
41101 dissatisfied Female disloyal Customer 37 Business travel Eco
41118 dissatisfied Male disloyal Customer 16 Business travel Eco
41124 dissatisfied Female disloyal Customer 47 Business travel Eco
41128 dissatisfied Male disloyal Customer 24 Business travel Eco
41136 dissatisfied Female disloyal Customer 36 Business travel Eco
41180 dissatisfied Male disloyal Customer 23 Business travel Eco
41185 dissatisfied Female disloyal Customer 8 Business travel Eco
41200 dissatisfied Female disloyal Customer 30 Business travel Eco
41207 dissatisfied Female disloyal Customer 37 Business travel Eco
41235 dissatisfied Male disloyal Customer 22 Business travel Eco
41253 dissatisfied Female disloyal Customer 18 Business travel Eco
41258 dissatisfied Male disloyal Customer 25 Business travel Business
41260 dissatisfied Male disloyal Customer 25 Business travel Business
41265 dissatisfied Female disloyal Customer 24 Business travel Eco
41308 dissatisfied Male disloyal Customer 23 Business travel Eco
41312 dissatisfied Female disloyal Customer 36 Business travel Eco
41324 dissatisfied Male disloyal Customer 28 Business travel Eco
41325 dissatisfied Male disloyal Customer 23 Business travel Eco
41344 dissatisfied Male disloyal Customer 26 Business travel Business
41368 dissatisfied Female disloyal Customer 26 Business travel Eco
41371 dissatisfied Male disloyal Customer 63 Business travel Eco
41381 dissatisfied Male disloyal Customer 47 Business travel Eco
41386 dissatisfied Female disloyal Customer 34 Business travel Eco
41389 dissatisfied Male disloyal Customer 23 Business travel Eco
41408 dissatisfied Female disloyal Customer 22 Business travel Eco Plus
41435 dissatisfied Male disloyal Customer 50 Business travel Eco
41436 dissatisfied Female disloyal Customer 51 Business travel Business
41437 dissatisfied Female disloyal Customer 51 Business travel Business
41440 dissatisfied Female disloyal Customer 50 Business travel Eco Plus
41447 dissatisfied Male disloyal Customer 48 Business travel Business
41507 dissatisfied Female disloyal Customer 18 Business travel Eco
41520 dissatisfied Male disloyal Customer 31 Business travel Eco
41521 dissatisfied Male disloyal Customer 37 Business travel Business
41523 dissatisfied Female disloyal Customer 22 Business travel Eco
41526 dissatisfied Female disloyal Customer 36 Business travel Eco Plus
41564 dissatisfied Male disloyal Customer 23 Business travel Eco
41566 dissatisfied Male disloyal Customer 24 Business travel Eco
41577 dissatisfied Male disloyal Customer 22 Business travel Eco
41580 dissatisfied Female disloyal Customer 30 Business travel Business
41587 dissatisfied Male disloyal Customer 38 Business travel Eco
41588 dissatisfied Male disloyal Customer 24 Business travel Eco
41600 dissatisfied Male disloyal Customer 29 Business travel Business
41613 dissatisfied Male disloyal Customer 27 Business travel Business
41620 dissatisfied Male disloyal Customer 22 Business travel Eco
41653 dissatisfied Female disloyal Customer 37 Business travel Eco
41668 dissatisfied Male disloyal Customer 62 Business travel Business
41680 dissatisfied Female disloyal Customer 52 Business travel Business
41702 dissatisfied Female disloyal Customer 35 Business travel Eco
41706 dissatisfied Male disloyal Customer 42 Business travel Eco
41727 dissatisfied Female disloyal Customer 41 Business travel Business
41733 dissatisfied Female disloyal Customer 24 Business travel Eco
41739 dissatisfied Female disloyal Customer 40 Business travel Business
41748 dissatisfied Male disloyal Customer 39 Business travel Business
41785 dissatisfied Male disloyal Customer 22 Business travel Eco
41790 dissatisfied Male disloyal Customer 49 Business travel Eco
41802 dissatisfied Male disloyal Customer 23 Business travel Eco
41812 dissatisfied Male disloyal Customer 36 Business travel Business
41817 dissatisfied Male disloyal Customer 35 Business travel Business
41830 dissatisfied Female disloyal Customer 34 Business travel Business
41837 dissatisfied Male disloyal Customer 34 Business travel Eco
41888 dissatisfied Female disloyal Customer 23 Business travel Eco
41906 dissatisfied Female disloyal Customer 38 Business travel Eco
41914 dissatisfied Male disloyal Customer 26 Business travel Business
41918 dissatisfied Female disloyal Customer 32 Business travel Eco
41924 dissatisfied Female disloyal Customer 20 Business travel Eco
41929 dissatisfied Female disloyal Customer 50 Business travel Eco
41934 dissatisfied Female disloyal Customer 78 Business travel Eco
41973 dissatisfied Male disloyal Customer 46 Business travel Eco
41980 dissatisfied Male disloyal Customer 59 Business travel Business
42003 dissatisfied Male disloyal Customer 54 Business travel Business
42043 dissatisfied Female disloyal Customer 47 Business travel Business
42048 dissatisfied Male disloyal Customer 46 Business travel Business
42049 dissatisfied Female disloyal Customer 46 Business travel Business
42054 dissatisfied Male disloyal Customer 27 Business travel Eco
42069 dissatisfied Male disloyal Customer 21 Business travel Eco
42119 dissatisfied Female disloyal Customer 40 Business travel Business
42126 dissatisfied Female disloyal Customer 40 Business travel Business
42129 dissatisfied Female disloyal Customer 40 Business travel Business
42130 dissatisfied Female disloyal Customer 40 Business travel Business
42145 dissatisfied Male disloyal Customer 39 Business travel Business
42169 dissatisfied Male disloyal Customer 39 Business travel Business
42184 dissatisfied Male disloyal Customer 14 Business travel Eco
42196 dissatisfied Female disloyal Customer 27 Business travel Eco
42198 dissatisfied Female disloyal Customer 23 Business travel Eco
42212 dissatisfied Female disloyal Customer 20 Business travel Eco
42223 dissatisfied Female disloyal Customer 20 Business travel Eco
42229 dissatisfied Male disloyal Customer 37 Business travel Business
42232 dissatisfied Female disloyal Customer 37 Business travel Eco Plus
42253 dissatisfied Female disloyal Customer 24 Business travel Eco
42269 dissatisfied Female disloyal Customer 29 Business travel Eco
42283 dissatisfied Female disloyal Customer 36 Business travel Business
42288 dissatisfied Female disloyal Customer 36 Business travel Business
42320 dissatisfied Male disloyal Customer 60 Business travel Eco
42337 dissatisfied Female disloyal Customer 22 Business travel Eco
42353 dissatisfied Female disloyal Customer 34 Business travel Eco Plus
42372 dissatisfied Male disloyal Customer 33 Business travel Business
42373 dissatisfied Female disloyal Customer 29 Business travel Eco
42375 dissatisfied Female disloyal Customer 33 Business travel Business
42377 dissatisfied Female disloyal Customer 24 Business travel Eco
42380 dissatisfied Male disloyal Customer 33 Business travel Business
42389 dissatisfied Female disloyal Customer 31 Business travel Eco
42392 dissatisfied Female disloyal Customer 22 Business travel Eco
42404 dissatisfied Female disloyal Customer 35 Business travel Eco
42410 dissatisfied Male disloyal Customer 24 Business travel Eco
42411 dissatisfied Male disloyal Customer 30 Business travel Business
42440 dissatisfied Female disloyal Customer 29 Business travel Eco
42447 dissatisfied Male disloyal Customer 29 Business travel Business
42449 dissatisfied Male disloyal Customer 38 Business travel Eco
42483 dissatisfied Female disloyal Customer 28 Business travel Business
42490 dissatisfied Female disloyal Customer 28 Business travel Business
42498 dissatisfied Female disloyal Customer 28 Business travel Eco Plus
42521 dissatisfied Male disloyal Customer 27 Business travel Business
42524 dissatisfied Female disloyal Customer 23 Business travel Eco
42527 dissatisfied Female disloyal Customer 27 Business travel Business
42561 dissatisfied Male disloyal Customer 17 Business travel Eco
42575 dissatisfied Female disloyal Customer 14 Business travel Eco
42589 dissatisfied Female disloyal Customer 18 Business travel Eco
42599 dissatisfied Female disloyal Customer 13 Business travel Eco
42608 dissatisfied Female disloyal Customer 16 Business travel Eco
42612 dissatisfied Female disloyal Customer 21 Business travel Eco
42621 dissatisfied Female disloyal Customer 35 Business travel Eco
42627 dissatisfied Female disloyal Customer 24 Business travel Eco
42630 dissatisfied Female disloyal Customer 23 Business travel Business
42634 dissatisfied Female disloyal Customer 36 Business travel Eco
42686 dissatisfied Female disloyal Customer 27 Business travel Eco
42695 dissatisfied Male disloyal Customer 40 Business travel Eco
42697 dissatisfied Female disloyal Customer 44 Business travel Eco
42698 dissatisfied Female disloyal Customer 25 Business travel Eco
42704 dissatisfied Female disloyal Customer 21 Business travel Eco
42719 dissatisfied Male disloyal Customer 61 Business travel Business
42726 dissatisfied Male disloyal Customer 58 Business travel Business
42728 dissatisfied Female disloyal Customer 29 Business travel Eco
42737 dissatisfied Female disloyal Customer 16 Business travel Eco
42747 dissatisfied Female disloyal Customer 51 Business travel Business
42748 dissatisfied Male disloyal Customer 50 Business travel Eco Plus
42769 dissatisfied Female disloyal Customer 11 Business travel Eco
42770 dissatisfied Female disloyal Customer 48 Business travel Business
42778 dissatisfied Male disloyal Customer 47 Business travel Business
42779 dissatisfied Male disloyal Customer 26 Business travel Eco
42780 dissatisfied Male disloyal Customer 47 Business travel Business
42785 dissatisfied Female disloyal Customer 46 Business travel Business
42797 dissatisfied Female disloyal Customer 45 Business travel Business
42820 dissatisfied Male disloyal Customer 22 Business travel Eco
42829 dissatisfied Male disloyal Customer 35 Business travel Eco
42860 dissatisfied Female disloyal Customer 39 Business travel Business
42866 dissatisfied Female disloyal Customer 39 Business travel Business
42872 dissatisfied Female disloyal Customer 39 Business travel Eco Plus
42877 dissatisfied Female disloyal Customer 23 Business travel Eco
42880 dissatisfied Male disloyal Customer 39 Business travel Business
42881 dissatisfied Female disloyal Customer 41 Business travel Eco
42887 dissatisfied Male disloyal Customer 23 Business travel Eco
42889 dissatisfied Male disloyal Customer 38 Business travel Eco Plus
42893 dissatisfied Female disloyal Customer 38 Business travel Business
42899 dissatisfied Male disloyal Customer 9 Business travel Eco
42949 dissatisfied Male disloyal Customer 37 Business travel Business
42950 dissatisfied Male disloyal Customer 37 Business travel Eco Plus
42961 dissatisfied Female disloyal Customer 36 Business travel Business
42967 dissatisfied Female disloyal Customer 36 Business travel Business
43002 dissatisfied Female disloyal Customer 11 Business travel Eco
43006 dissatisfied Male disloyal Customer 35 Business travel Business
43020 dissatisfied Female disloyal Customer 25 Business travel Eco
43063 dissatisfied Male disloyal Customer 27 Business travel Eco
43074 dissatisfied Male disloyal Customer 20 Business travel Eco
43076 dissatisfied Female disloyal Customer 24 Business travel Eco
43079 dissatisfied Female disloyal Customer 38 Business travel Eco
43115 dissatisfied Female disloyal Customer 29 Business travel Business
43138 dissatisfied Male disloyal Customer 38 Business travel Eco
43143 dissatisfied Male disloyal Customer 35 Business travel Eco
43145 dissatisfied Female disloyal Customer 28 Business travel Business
43153 dissatisfied Female disloyal Customer 24 Business travel Eco
43172 dissatisfied Male disloyal Customer 11 Business travel Eco
43184 dissatisfied Female disloyal Customer 27 Business travel Business
43200 dissatisfied Male disloyal Customer 27 Business travel Business
43219 dissatisfied Male disloyal Customer 26 Business travel Business
43221 dissatisfied Male disloyal Customer 24 Business travel Eco
43246 dissatisfied Female disloyal Customer 50 Business travel Eco
43255 dissatisfied Male disloyal Customer 16 Business travel Eco
43256 dissatisfied Male disloyal Customer 45 Business travel Eco
43272 dissatisfied Male disloyal Customer 59 Business travel Eco
43311 dissatisfied Female disloyal Customer 50 Business travel Business
43356 dissatisfied Male disloyal Customer 36 Business travel Business
43357 dissatisfied Male disloyal Customer 36 Business travel Business
43363 dissatisfied Male disloyal Customer 25 Business travel Eco
43369 dissatisfied Male disloyal Customer 35 Business travel Business
43406 dissatisfied Male disloyal Customer 28 Business travel Business
43430 dissatisfied Male disloyal Customer 26 Business travel Eco
43434 dissatisfied Male disloyal Customer 7 Business travel Eco
43448 dissatisfied Male disloyal Customer 24 Business travel Eco
43449 dissatisfied Male disloyal Customer 25 Business travel Eco
43472 dissatisfied Male disloyal Customer 22 Business travel Eco
43480 dissatisfied Male disloyal Customer 24 Business travel Eco
43485 dissatisfied Female disloyal Customer 26 Business travel Business
43490 dissatisfied Male disloyal Customer 26 Business travel Business
43498 dissatisfied Female disloyal Customer 27 Business travel Eco
43518 dissatisfied Male disloyal Customer 26 Business travel Eco
43520 dissatisfied Female disloyal Customer 23 Business travel Eco Plus
43536 dissatisfied Female disloyal Customer 22 Business travel Business
43539 dissatisfied Female disloyal Customer 34 Business travel Eco
43563 dissatisfied Male disloyal Customer 16 Business travel Eco
43564 dissatisfied Male disloyal Customer 37 Business travel Eco
43570 dissatisfied Female disloyal Customer 37 Business travel Eco
43575 dissatisfied Female disloyal Customer 20 Business travel Eco
43600 dissatisfied Female disloyal Customer 15 Business travel Eco
43601 dissatisfied Male disloyal Customer 11 Business travel Eco
43606 dissatisfied Female disloyal Customer 34 Business travel Eco
43622 dissatisfied Male disloyal Customer 24 Business travel Eco
43659 dissatisfied Male disloyal Customer 30 Business travel Eco
43679 dissatisfied Male disloyal Customer 27 Business travel Eco
43689 dissatisfied Female disloyal Customer 28 Business travel Eco
43690 dissatisfied Female disloyal Customer 30 Business travel Business
43694 dissatisfied Female disloyal Customer 27 Business travel Business
43708 dissatisfied Male disloyal Customer 36 Business travel Business
43713 dissatisfied Male disloyal Customer 28 Business travel Eco
43726 dissatisfied Male disloyal Customer 9 Business travel Eco
43761 dissatisfied Female disloyal Customer 30 Business travel Eco
43776 dissatisfied Female disloyal Customer 38 Business travel Eco
43782 dissatisfied Female disloyal Customer 27 Business travel Business
43787 dissatisfied Male disloyal Customer 24 Business travel Eco
43793 dissatisfied Male disloyal Customer 37 Business travel Eco
43811 dissatisfied Female disloyal Customer 20 Business travel Eco
43817 dissatisfied Female disloyal Customer 21 Business travel Eco
43835 dissatisfied Male disloyal Customer 27 Business travel Business
43838 dissatisfied Male disloyal Customer 23 Business travel Eco
43852 dissatisfied Male disloyal Customer 37 Business travel Eco
43857 dissatisfied Male disloyal Customer 14 Business travel Eco Plus
43918 dissatisfied Female disloyal Customer 26 Business travel Eco
43927 dissatisfied Female disloyal Customer 24 Business travel Eco
43932 dissatisfied Female disloyal Customer 23 Business travel Business
43953 dissatisfied Male disloyal Customer 21 Business travel Business
43957 dissatisfied Female disloyal Customer 48 Business travel Eco
43961 dissatisfied Female disloyal Customer 47 Business travel Eco
43971 dissatisfied Female disloyal Customer 38 Business travel Eco
43983 dissatisfied Female disloyal Customer 26 Business travel Eco
44012 dissatisfied Male disloyal Customer 36 Business travel Eco
44015 dissatisfied Male disloyal Customer 23 Business travel Eco
44024 dissatisfied Male disloyal Customer 44 Business travel Eco
44030 dissatisfied Female disloyal Customer 25 Business travel Eco
44044 dissatisfied Male disloyal Customer 21 Business travel Eco
44045 dissatisfied Female disloyal Customer 39 Business travel Eco
44066 dissatisfied Female disloyal Customer 38 Business travel Eco
44112 dissatisfied Male disloyal Customer 26 Business travel Business
44129 dissatisfied Female disloyal Customer 22 Business travel Eco
44139 dissatisfied Female disloyal Customer 56 Business travel Eco
44146 dissatisfied Male disloyal Customer 26 Business travel Business
44150 dissatisfied Male disloyal Customer 27 Business travel Eco
44175 dissatisfied Female disloyal Customer 25 Business travel Eco
44184 dissatisfied Male disloyal Customer 23 Business travel Eco
44185 dissatisfied Female disloyal Customer 20 Business travel Eco
44227 dissatisfied Female disloyal Customer 20 Business travel Business
44249 dissatisfied Male disloyal Customer 26 Business travel Business
44262 dissatisfied Female disloyal Customer 25 Business travel Business
44274 dissatisfied Female disloyal Customer 30 Business travel Eco
44275 dissatisfied Male disloyal Customer 32 Business travel Eco
44303 dissatisfied Female disloyal Customer 38 Business travel Eco
44311 dissatisfied Male disloyal Customer 25 Business travel Eco
44322 dissatisfied Male disloyal Customer 22 Business travel Eco
44331 dissatisfied Male disloyal Customer 38 Business travel Eco
44335 dissatisfied Male disloyal Customer 54 Business travel Eco
44343 dissatisfied Female disloyal Customer 25 Business travel Eco
44350 dissatisfied Male disloyal Customer 20 Business travel Eco
44359 dissatisfied Female disloyal Customer 34 Business travel Eco
44368 dissatisfied Female disloyal Customer 18 Business travel Eco
44399 dissatisfied Female disloyal Customer 54 Business travel Eco
44429 dissatisfied Female disloyal Customer 25 Business travel Business
44432 dissatisfied Female disloyal Customer 25 Business travel Business
44440 dissatisfied Male disloyal Customer 26 Business travel Eco
44445 dissatisfied Female disloyal Customer 23 Business travel Eco
44455 dissatisfied Male disloyal Customer 27 Business travel Eco
44467 dissatisfied Female disloyal Customer 25 Business travel Business
44469 dissatisfied Male disloyal Customer 23 Business travel Eco
44480 dissatisfied Female disloyal Customer 46 Business travel Eco
44494 dissatisfied Female disloyal Customer 41 Business travel Eco
44535 dissatisfied Female disloyal Customer 26 Business travel Eco
44537 dissatisfied Female disloyal Customer 35 Business travel Eco
44543 dissatisfied Male disloyal Customer 35 Business travel Eco
44559 dissatisfied Female disloyal Customer 26 Business travel Eco
44565 dissatisfied Male disloyal Customer 38 Business travel Eco
44569 dissatisfied Female disloyal Customer 22 Business travel Eco
44574 dissatisfied Male disloyal Customer 22 Business travel Eco
44577 dissatisfied Male disloyal Customer 51 Business travel Eco
44584 dissatisfied Male disloyal Customer 25 Business travel Business
44589 dissatisfied Female disloyal Customer 25 Business travel Business
44599 dissatisfied Male disloyal Customer 34 Business travel Eco
44606 dissatisfied Female disloyal Customer 22 Business travel Eco
44638 dissatisfied Female disloyal Customer 27 Business travel Eco
44643 dissatisfied Male disloyal Customer 36 Business travel Eco
44655 dissatisfied Female disloyal Customer 23 Business travel Eco
44657 dissatisfied Male disloyal Customer 23 Business travel Eco
44665 dissatisfied Female disloyal Customer 56 Business travel Eco
44683 dissatisfied Male disloyal Customer 34 Business travel Eco
44715 dissatisfied Female disloyal Customer 25 Business travel Business
44716 dissatisfied Female disloyal Customer 22 Business travel Eco
44726 dissatisfied Male disloyal Customer 25 Business travel Business
44735 dissatisfied Male disloyal Customer 20 Business travel Eco
44737 dissatisfied Female disloyal Customer 23 Business travel Eco
44743 dissatisfied Female disloyal Customer 34 Business travel Eco
44745 dissatisfied Female disloyal Customer 47 Business travel Eco
44767 dissatisfied Female disloyal Customer 25 Business travel Eco
44779 dissatisfied Male disloyal Customer 24 Business travel Eco
44807 dissatisfied Female disloyal Customer 30 Business travel Eco
44824 dissatisfied Male disloyal Customer 37 Business travel Eco
44842 dissatisfied Male disloyal Customer 20 Business travel Eco
44851 dissatisfied Male disloyal Customer 25 Business travel Business
44867 dissatisfied Female disloyal Customer 23 Business travel Eco
44909 dissatisfied Female disloyal Customer 24 Business travel Eco
44911 dissatisfied Male disloyal Customer 19 Business travel Eco
44927 dissatisfied Male disloyal Customer 27 Business travel Eco
44928 dissatisfied Female disloyal Customer 37 Business travel Eco
44932 dissatisfied Female disloyal Customer 30 Business travel Eco
44942 dissatisfied Male disloyal Customer 26 Business travel Eco
44947 dissatisfied Male disloyal Customer 35 Business travel Eco
44948 dissatisfied Male disloyal Customer 20 Business travel Eco
44956 dissatisfied Male disloyal Customer 23 Business travel Eco
44962 dissatisfied Female disloyal Customer 21 Business travel Eco
44969 dissatisfied Male disloyal Customer 41 Business travel Eco
44975 dissatisfied Female disloyal Customer 39 Business travel Eco
44981 dissatisfied Male disloyal Customer 25 Business travel Eco
45003 dissatisfied Male disloyal Customer 25 Business travel Business
45005 dissatisfied Male disloyal Customer 25 Business travel Business
45017 dissatisfied Female disloyal Customer 23 Business travel Eco
45018 dissatisfied Female disloyal Customer 25 Business travel Business
45034 dissatisfied Female disloyal Customer 34 Business travel Eco
45050 dissatisfied Female disloyal Customer 25 Business travel Eco
45065 dissatisfied Male disloyal Customer 20 Business travel Eco
45077 dissatisfied Female disloyal Customer 38 Business travel Eco
45090 dissatisfied Female disloyal Customer 54 Business travel Eco
45097 dissatisfied Female disloyal Customer 36 Business travel Eco
45161 dissatisfied Female disloyal Customer 27 Business travel Eco
45184 dissatisfied Female disloyal Customer 24 Business travel Eco
45185 dissatisfied Female disloyal Customer 21 Business travel Eco
45198 dissatisfied Female disloyal Customer 21 Business travel Eco
45207 dissatisfied Male disloyal Customer 36 Business travel Eco
45266 dissatisfied Male disloyal Customer 22 Business travel Eco
45267 dissatisfied Female disloyal Customer 23 Business travel Eco
45284 dissatisfied Female disloyal Customer 26 Business travel Eco
45339 dissatisfied Male disloyal Customer 29 Business travel Business
45363 dissatisfied Male disloyal Customer 22 Business travel Eco
45369 dissatisfied Female disloyal Customer 27 Business travel Eco
45382 dissatisfied Male disloyal Customer 24 Business travel Eco
45387 dissatisfied Male disloyal Customer 37 Business travel Eco
45388 dissatisfied Female disloyal Customer 36 Business travel Eco Plus
45409 dissatisfied Male disloyal Customer 27 Business travel Eco
45411 dissatisfied Female disloyal Customer 48 Business travel Eco
45412 dissatisfied Male disloyal Customer 37 Business travel Eco
45421 dissatisfied Female disloyal Customer 25 Business travel Eco
45427 dissatisfied Female disloyal Customer 22 Business travel Eco Plus
45436 dissatisfied Male disloyal Customer 26 Business travel Eco
45459 dissatisfied Male disloyal Customer 24 Business travel Eco
45465 dissatisfied Male disloyal Customer 36 Business travel Business
45466 dissatisfied Female disloyal Customer 35 Business travel Eco
45467 dissatisfied Male disloyal Customer 36 Business travel Business
45509 dissatisfied Male disloyal Customer 44 Business travel Business
45518 dissatisfied Male disloyal Customer 27 Business travel Business
45525 dissatisfied Male disloyal Customer 33 Business travel Eco
45543 dissatisfied Female disloyal Customer 36 Business travel Eco
45544 dissatisfied Female disloyal Customer 30 Business travel Eco
45550 dissatisfied Female disloyal Customer 27 Business travel Eco
45558 dissatisfied Male disloyal Customer 34 Business travel Eco
45569 dissatisfied Male disloyal Customer 27 Business travel Eco
45580 dissatisfied Female disloyal Customer 29 Business travel Eco
45609 dissatisfied Female disloyal Customer 26 Business travel Eco
45627 dissatisfied Female disloyal Customer 23 Business travel Business
45633 dissatisfied Female disloyal Customer 32 Business travel Eco
45640 dissatisfied Female disloyal Customer 33 Business travel Eco
45653 dissatisfied Female disloyal Customer 37 Business travel Eco
45660 dissatisfied Female disloyal Customer 35 Business travel Eco
45687 dissatisfied Male disloyal Customer 40 Business travel Eco
45694 dissatisfied Female disloyal Customer 24 Business travel Eco
45710 dissatisfied Male disloyal Customer 50 Business travel Eco
45717 dissatisfied Female disloyal Customer 24 Business travel Business
45725 dissatisfied Male disloyal Customer 37 Business travel Eco
45734 dissatisfied Female disloyal Customer 23 Business travel Business
45739 dissatisfied Female disloyal Customer 37 Business travel Eco
45746 dissatisfied Male disloyal Customer 27 Business travel Eco
45776 dissatisfied Female disloyal Customer 36 Business travel Eco
45795 dissatisfied Male disloyal Customer 19 Business travel Eco
45810 dissatisfied Female disloyal Customer 74 Business travel Eco
45815 dissatisfied Male disloyal Customer 47 Business travel Eco
45827 dissatisfied Male disloyal Customer 16 Business travel Eco
45837 dissatisfied Male disloyal Customer 40 Business travel Eco
45856 dissatisfied Female disloyal Customer 23 Business travel Eco
45868 dissatisfied Female disloyal Customer 50 Business travel Business
45887 dissatisfied Female disloyal Customer 45 Business travel Business
45913 dissatisfied Male disloyal Customer 24 Business travel Eco
45918 dissatisfied Female disloyal Customer 40 Business travel Business
45950 dissatisfied Male disloyal Customer 23 Business travel Eco
45954 dissatisfied Female disloyal Customer 38 Business travel Eco
45957 satisfied Male disloyal Customer 38 Business travel Business
45958 dissatisfied Female disloyal Customer 38 Business travel Business
45968 dissatisfied Female disloyal Customer 38 Business travel Business
45973 dissatisfied Male disloyal Customer 30 Business travel Eco
46008 dissatisfied Male disloyal Customer 36 Business travel Business
46015 dissatisfied Female disloyal Customer 36 Business travel Business
46032 dissatisfied Male disloyal Customer 28 Business travel Eco
46040 dissatisfied Female disloyal Customer 11 Business travel Eco
46052 dissatisfied Male disloyal Customer 34 Business travel Business
46058 dissatisfied Male disloyal Customer 36 Business travel Eco
46061 dissatisfied Female disloyal Customer 34 Business travel Business
46081 dissatisfied Female disloyal Customer 33 Business travel Business
46093 dissatisfied Male disloyal Customer 32 Business travel Business
46100 dissatisfied Female disloyal Customer 32 Business travel Business
46104 dissatisfied Female disloyal Customer 32 Business travel Eco Plus
46105 dissatisfied Male disloyal Customer 32 Business travel Business
46120 dissatisfied Male disloyal Customer 30 Business travel Business
46124 dissatisfied Male disloyal Customer 11 Business travel Eco
46143 dissatisfied Male disloyal Customer 25 Business travel Eco
46173 dissatisfied Male disloyal Customer 28 Business travel Business
46179 dissatisfied Male disloyal Customer 30 Business travel Eco
46190 dissatisfied Female disloyal Customer 27 Business travel Business
46213 dissatisfied Male disloyal Customer 29 Business travel Eco
46226 dissatisfied Male disloyal Customer 26 Business travel Business
46227 dissatisfied Female disloyal Customer 20 Business travel Eco
46240 dissatisfied Male disloyal Customer 20 Business travel Eco
46266 dissatisfied Male disloyal Customer 12 Business travel Eco
46271 dissatisfied Male disloyal Customer 23 Business travel Eco
46277 dissatisfied Male disloyal Customer 71 Business travel Eco
46317 dissatisfied Female disloyal Customer 15 Business travel Eco
46326 dissatisfied Female disloyal Customer 44 Business travel Eco Plus
46328 dissatisfied Female disloyal Customer 25 Business travel Eco
46329 dissatisfied Female disloyal Customer 24 Business travel Eco
46330 dissatisfied Male disloyal Customer 44 Business travel Business
46331 dissatisfied Male disloyal Customer 44 Business travel Business
46338 dissatisfied Male disloyal Customer 35 Business travel Eco
46356 dissatisfied Male disloyal Customer 41 Business travel Business
46364 dissatisfied Female disloyal Customer 25 Business travel Eco
46373 dissatisfied Female disloyal Customer 39 Business travel Business
46410 dissatisfied Female disloyal Customer 23 Business travel Eco
46443 dissatisfied Male disloyal Customer 49 Business travel Eco
46445 dissatisfied Female disloyal Customer 43 Business travel Eco
46447 dissatisfied Male disloyal Customer 36 Business travel Business
46464 dissatisfied Male disloyal Customer 57 Business travel Eco
46467 dissatisfied Female disloyal Customer 25 Business travel Eco
46473 dissatisfied Male disloyal Customer 36 Business travel Business
46498 dissatisfied Male disloyal Customer 35 Business travel Business
46500 dissatisfied Male disloyal Customer 35 Business travel Eco Plus
46516 dissatisfied Male disloyal Customer 34 Business travel Business
46518 dissatisfied Male disloyal Customer 34 Business travel Business
46569 dissatisfied Male disloyal Customer 49 Business travel Eco
46572 dissatisfied Male disloyal Customer 37 Business travel Eco
46574 dissatisfied Female disloyal Customer 15 Business travel Eco
46576 dissatisfied Male disloyal Customer 30 Business travel Business
46602 dissatisfied Female disloyal Customer 21 Business travel Eco
46618 dissatisfied Male disloyal Customer 21 Business travel Eco
46643 dissatisfied Female disloyal Customer 23 Business travel Eco
46668 dissatisfied Male disloyal Customer 23 Business travel Eco
46684 dissatisfied Male disloyal Customer 26 Business travel Business
46712 dissatisfied Male disloyal Customer 36 Business travel Eco
46714 dissatisfied Female disloyal Customer 20 Business travel Eco
46716 dissatisfied Female disloyal Customer 25 Business travel Eco
46726 dissatisfied Male disloyal Customer 23 Business travel Eco
46744 dissatisfied Female disloyal Customer 66 Business travel Eco Plus
46746 satisfied Male disloyal Customer 65 Business travel Business
46788 dissatisfied Male disloyal Customer 33 Business travel Eco
46807 dissatisfied Female disloyal Customer 56 Business travel Eco Plus
46815 dissatisfied Male disloyal Customer 28 Business travel Eco
46826 dissatisfied Male disloyal Customer 54 Business travel Business
46827 dissatisfied Female disloyal Customer 54 Business travel Business
46842 satisfied Male disloyal Customer 52 Business travel Business
46843 dissatisfied Female disloyal Customer 52 Business travel Business
46846 dissatisfied Female disloyal Customer 52 Business travel Business
46847 dissatisfied Female disloyal Customer 27 Business travel Eco
46879 dissatisfied Female disloyal Customer 24 Business travel Eco
46886 dissatisfied Male disloyal Customer 49 Business travel Business
46894 dissatisfied Female disloyal Customer 46 Business travel Eco
46896 dissatisfied Female disloyal Customer 48 Business travel Business
46901 dissatisfied Female disloyal Customer 25 Business travel Eco
46902 dissatisfied Male disloyal Customer 48 Business travel Business
46906 dissatisfied Female disloyal Customer 47 Business travel Eco Plus
46907 dissatisfied Female disloyal Customer 47 Business travel Eco Plus
46912 dissatisfied Female disloyal Customer 23 Business travel Eco
46915 satisfied Male disloyal Customer 46 Business travel Business
46934 dissatisfied Female disloyal Customer 43 Business travel Eco
46961 dissatisfied Female disloyal Customer 44 Business travel Business
46990 dissatisfied Male disloyal Customer 41 Business travel Eco
46998 dissatisfied Female disloyal Customer 22 Business travel Eco
47009 dissatisfied Female disloyal Customer 42 Business travel Business
47019 dissatisfied Female disloyal Customer 42 Business travel Business
47030 dissatisfied Male disloyal Customer 41 Business travel Eco
47034 dissatisfied Male disloyal Customer 10 Business travel Eco
47045 dissatisfied Female disloyal Customer 41 Business travel Business
47077 dissatisfied Female disloyal Customer 40 Business travel Business
47087 satisfied Male disloyal Customer 39 Business travel Business
47090 dissatisfied Female disloyal Customer 50 Business travel Eco
47111 dissatisfied Female disloyal Customer 39 Business travel Business
47118 dissatisfied Female disloyal Customer 13 Business travel Eco
47130 dissatisfied Male disloyal Customer 26 Business travel Eco
47157 satisfied Male disloyal Customer 21 Business travel Eco
47162 dissatisfied Female disloyal Customer 57 Business travel Eco
47169 dissatisfied Male disloyal Customer 14 Business travel Eco
47171 dissatisfied Male disloyal Customer 20 Business travel Eco
47217 dissatisfied Female disloyal Customer 37 Business travel Eco Plus
47225 dissatisfied Female disloyal Customer 37 Business travel Business
47232 dissatisfied Female disloyal Customer 15 Business travel Eco
47240 dissatisfied Female disloyal Customer 37 Business travel Eco
47251 dissatisfied Male disloyal Customer 37 Business travel Business
47256 dissatisfied Male disloyal Customer 37 Business travel Business
47264 dissatisfied Male disloyal Customer 37 Business travel Business
47275 dissatisfied Male disloyal Customer 36 Business travel Business
47281 dissatisfied Male disloyal Customer 25 Business travel Eco
47290 satisfied Male disloyal Customer 23 Business travel Eco
47298 dissatisfied Female disloyal Customer 36 Business travel Business
47304 dissatisfied Female disloyal Customer 23 Business travel Eco
47316 dissatisfied Female disloyal Customer 59 Business travel Eco
47321 dissatisfied Male disloyal Customer 36 Business travel Business
47322 dissatisfied Male disloyal Customer 20 Business travel Eco
47326 dissatisfied Male disloyal Customer 36 Business travel Eco Plus
47328 dissatisfied Female disloyal Customer 26 Business travel Eco
47343 dissatisfied Female disloyal Customer 11 Business travel Eco
47348 dissatisfied Female disloyal Customer 26 Business travel Eco
47354 dissatisfied Male disloyal Customer 35 Business travel Business
47355 dissatisfied Male disloyal Customer 35 Business travel Business
47356 dissatisfied Male disloyal Customer 35 Business travel Business
47394 dissatisfied Male disloyal Customer 35 Business travel Business
47401 dissatisfied Male disloyal Customer 34 Business travel Business
47403 dissatisfied Male disloyal Customer 28 Business travel Eco
47427 dissatisfied Female disloyal Customer 34 Business travel Eco Plus
47439 dissatisfied Female disloyal Customer 38 Business travel Eco
47452 dissatisfied Female disloyal Customer 26 Business travel Eco
47454 dissatisfied Female disloyal Customer 33 Business travel Eco Plus
47463 dissatisfied Female disloyal Customer 21 Business travel Eco
47502 dissatisfied Female disloyal Customer 28 Business travel Eco
47513 dissatisfied Female disloyal Customer 72 Business travel Eco
47536 dissatisfied Female disloyal Customer 16 Business travel Eco
47549 dissatisfied Female disloyal Customer 20 Business travel Eco
47571 dissatisfied Female disloyal Customer 30 Business travel Eco Plus
47576 dissatisfied Female disloyal Customer 30 Business travel Business
47585 dissatisfied Female disloyal Customer 20 Business travel Eco
47591 dissatisfied Female disloyal Customer 29 Business travel Eco
47603 dissatisfied Male disloyal Customer 25 Business travel Eco
47606 dissatisfied Male disloyal Customer 13 Business travel Eco
47607 dissatisfied Male disloyal Customer 29 Business travel Eco Plus
47678 dissatisfied Female disloyal Customer 28 Business travel Business
47717 dissatisfied Male disloyal Customer 29 Business travel Eco
47722 dissatisfied Male disloyal Customer 24 Business travel Eco
47724 dissatisfied Female disloyal Customer 9 Business travel Eco
47729 dissatisfied Female disloyal Customer 27 Business travel Business
47738 dissatisfied Male disloyal Customer 26 Business travel Eco
47745 dissatisfied Male disloyal Customer 27 Business travel Business
47747 dissatisfied Female disloyal Customer 27 Business travel Business
47784 dissatisfied Male disloyal Customer 27 Business travel Business
47817 dissatisfied Female disloyal Customer 43 Business travel Eco
47822 dissatisfied Male disloyal Customer 26 Business travel Business
47827 dissatisfied Female disloyal Customer 23 Business travel Eco
47830 dissatisfied Male disloyal Customer 26 Business travel Business
47845 dissatisfied Male disloyal Customer 21 Business travel Eco
47848 dissatisfied Male disloyal Customer 11 Business travel Eco
47896 dissatisfied Male disloyal Customer 27 Business travel Eco
47897 dissatisfied Male disloyal Customer 37 Business travel Eco
47900 dissatisfied Female disloyal Customer 59 Business travel Eco
47909 dissatisfied Female disloyal Customer 21 Business travel Eco
47914 dissatisfied Male disloyal Customer 24 Business travel Eco
47932 dissatisfied Female disloyal Customer 27 Business travel Eco
47951 dissatisfied Male disloyal Customer 23 Business travel Eco
47965 dissatisfied Female disloyal Customer 20 Business travel Eco
47969 dissatisfied Female disloyal Customer 23 Business travel Eco
47974 dissatisfied Female disloyal Customer 17 Business travel Eco
47984 dissatisfied Male disloyal Customer 20 Business travel Eco Plus
47989 dissatisfied Male disloyal Customer 24 Business travel Eco
47990 dissatisfied Male disloyal Customer 26 Business travel Eco
47992 dissatisfied Male disloyal Customer 24 Business travel Eco
48006 dissatisfied Female disloyal Customer 23 Business travel Eco
48025 dissatisfied Male disloyal Customer 25 Business travel Eco
48028 dissatisfied Male disloyal Customer 29 Business travel Eco
48031 dissatisfied Female disloyal Customer 43 Business travel Eco
48047 dissatisfied Female disloyal Customer 25 Business travel Eco
48068 satisfied Male disloyal Customer 62 Business travel Business
48100 dissatisfied Male disloyal Customer 57 Business travel Eco Plus
48102 dissatisfied Female disloyal Customer 55 Business travel Eco
48111 dissatisfied Male disloyal Customer 43 Business travel Eco
48120 dissatisfied Female disloyal Customer 31 Business travel Eco
48129 dissatisfied Male disloyal Customer 29 Business travel Eco
48131 dissatisfied Male disloyal Customer 22 Business travel Eco
48140 dissatisfied Male disloyal Customer 50 Business travel Business
48161 dissatisfied Male disloyal Customer 30 Business travel Eco
48189 dissatisfied Female disloyal Customer 45 Business travel Business
48195 dissatisfied Female disloyal Customer 54 Business travel Eco
48196 dissatisfied Female disloyal Customer 48 Business travel Eco
48218 dissatisfied Female disloyal Customer 44 Business travel Business
48327 dissatisfied Female disloyal Customer 39 Business travel Business
48333 dissatisfied Female disloyal Customer 39 Business travel Business
48356 dissatisfied Male disloyal Customer 39 Business travel Business
48359 dissatisfied Male disloyal Customer 39 Business travel Business
48369 dissatisfied Female disloyal Customer 27 Business travel Eco
48380 dissatisfied Female disloyal Customer 38 Business travel Business
48392 dissatisfied Female disloyal Customer 25 Business travel Eco
48403 dissatisfied Male disloyal Customer 38 Business travel Business
48405 dissatisfied Male disloyal Customer 18 Business travel Eco
48407 satisfied Female disloyal Customer 38 Business travel Business
48414 satisfied Female disloyal Customer 28 Business travel Eco
48422 dissatisfied Female disloyal Customer 37 Business travel Business
48460 dissatisfied Male disloyal Customer 17 Business travel Eco
48471 dissatisfied Female disloyal Customer 36 Business travel Business
48478 dissatisfied Female disloyal Customer 43 Business travel Eco
48492 dissatisfied Male disloyal Customer 25 Business travel Eco
48497 dissatisfied Male disloyal Customer 22 Business travel Eco
48505 dissatisfied Male disloyal Customer 11 Business travel Eco
48547 dissatisfied Male disloyal Customer 21 Business travel Eco
48551 dissatisfied Male disloyal Customer 56 Business travel Eco
48555 dissatisfied Male disloyal Customer 19 Business travel Eco
48559 dissatisfied Male disloyal Customer 35 Business travel Business
48563 dissatisfied Male disloyal Customer 35 Business travel Business
48575 dissatisfied Female disloyal Customer 30 Business travel Eco
48583 dissatisfied Female disloyal Customer 9 Business travel Eco
48602 dissatisfied Female disloyal Customer 22 Business travel Eco
48613 dissatisfied Male disloyal Customer 33 Business travel Business
48621 dissatisfied Male disloyal Customer 32 Business travel Business
48645 dissatisfied Male disloyal Customer 19 Business travel Eco
48664 dissatisfied Male disloyal Customer 16 Business travel Eco
48667 dissatisfied Female disloyal Customer 9 Business travel Eco
48675 dissatisfied Male disloyal Customer 20 Business travel Eco
48676 dissatisfied Male disloyal Customer 28 Business travel Eco
48689 dissatisfied Female disloyal Customer 27 Business travel Eco
48696 dissatisfied Male disloyal Customer 29 Business travel Business
48723 dissatisfied Female disloyal Customer 29 Business travel Eco Plus
48759 dissatisfied Male disloyal Customer 12 Business travel Eco
48768 dissatisfied Male disloyal Customer 38 Business travel Eco
48815 dissatisfied Female disloyal Customer 24 Business travel Eco
48817 dissatisfied Female disloyal Customer 10 Business travel Eco
48854 dissatisfied Female disloyal Customer 29 Business travel Eco
48855 dissatisfied Male disloyal Customer 27 Business travel Eco Plus
48875 dissatisfied Male disloyal Customer 32 Business travel Eco
48883 dissatisfied Female disloyal Customer 25 Business travel Eco
48910 dissatisfied Female disloyal Customer 37 Business travel Eco
48931 dissatisfied Female disloyal Customer 15 Business travel Eco
48961 dissatisfied Male disloyal Customer 17 Business travel Eco
48974 dissatisfied Female disloyal Customer 24 Business travel Eco
48993 dissatisfied Male disloyal Customer 21 Business travel Eco
49001 dissatisfied Female disloyal Customer 20 Business travel Eco Plus
49005 dissatisfied Female disloyal Customer 17 Business travel Eco
49006 dissatisfied Male disloyal Customer 24 Business travel Eco
49017 dissatisfied Female disloyal Customer 18 Business travel Eco Plus
49021 dissatisfied Male disloyal Customer 37 Business travel Eco
49044 dissatisfied Female disloyal Customer 62 Business travel Business
49064 dissatisfied Male disloyal Customer 46 Business travel Business
49077 dissatisfied Female disloyal Customer 44 Business travel Eco
49090 dissatisfied Female disloyal Customer 40 Business travel Business
49114 dissatisfied Male disloyal Customer 37 Business travel Business
49115 dissatisfied Male disloyal Customer 37 Business travel Business
49118 dissatisfied Male disloyal Customer 36 Business travel Business
49119 dissatisfied Male disloyal Customer 36 Business travel Business
49123 dissatisfied Female disloyal Customer 36 Business travel Eco Plus
49129 dissatisfied Male disloyal Customer 36 Business travel Business
49158 dissatisfied Male disloyal Customer 20 Business travel Eco
49166 dissatisfied Female disloyal Customer 24 Business travel Eco
49215 dissatisfied Male disloyal Customer 26 Business travel Business
49239 dissatisfied Female disloyal Customer 25 Business travel Eco
49258 dissatisfied Female disloyal Customer 30 Business travel Eco
49280 dissatisfied Male disloyal Customer 45 Business travel Eco
49307 dissatisfied Female disloyal Customer 27 Business travel Eco
49312 dissatisfied Male disloyal Customer 36 Business travel Eco
49317 dissatisfied Female disloyal Customer 43 Business travel Eco
49325 dissatisfied Female disloyal Customer 37 Business travel Eco
49328 dissatisfied Male disloyal Customer 39 Business travel Eco
49343 dissatisfied Male disloyal Customer 50 Business travel Eco
49350 dissatisfied Male disloyal Customer 27 Business travel Eco
49354 dissatisfied Male disloyal Customer 26 Business travel Eco
49362 dissatisfied Female disloyal Customer 41 Business travel Eco
49363 dissatisfied Female disloyal Customer 25 Business travel Eco Plus
49388 dissatisfied Male disloyal Customer 56 Business travel Eco
49416 dissatisfied Female disloyal Customer 27 Business travel Eco
49421 dissatisfied Male disloyal Customer 21 Business travel Eco
49429 dissatisfied Male disloyal Customer 47 Business travel Eco
49445 dissatisfied Female disloyal Customer 22 Business travel Eco
49446 dissatisfied Female disloyal Customer 44 Business travel Eco
49458 dissatisfied Female disloyal Customer 19 Business travel Eco
49460 dissatisfied Female disloyal Customer 38 Business travel Eco
49476 dissatisfied Female disloyal Customer 26 Business travel Eco
49501 dissatisfied Male disloyal Customer 9 Business travel Eco
49521 dissatisfied Male disloyal Customer 26 Business travel Eco
49527 dissatisfied Male disloyal Customer 26 Business travel Eco
49533 dissatisfied Female disloyal Customer 34 Business travel Eco
49552 dissatisfied Male disloyal Customer 26 Business travel Eco
49564 dissatisfied Female disloyal Customer 33 Business travel Eco
49579 dissatisfied Female disloyal Customer 44 Business travel Eco
49636 dissatisfied Female disloyal Customer 44 Business travel Business
49638 dissatisfied Male disloyal Customer 40 Business travel Eco Plus
49642 dissatisfied Female disloyal Customer 24 Business travel Eco
49648 dissatisfied Male disloyal Customer 37 Business travel Eco
49656 dissatisfied Female disloyal Customer 33 Business travel Business
49666 dissatisfied Male disloyal Customer 59 Business travel Business
49667 dissatisfied Female disloyal Customer 46 Business travel Eco
49680 dissatisfied Female disloyal Customer 46 Business travel Business
49723 dissatisfied Male disloyal Customer 47 Business travel Eco
49727 dissatisfied Female disloyal Customer 34 Business travel Eco Plus
49754 dissatisfied Male disloyal Customer 30 Business travel Eco
49760 dissatisfied Female disloyal Customer 23 Business travel Eco
49767 dissatisfied Male disloyal Customer 22 Business travel Eco
49772 dissatisfied Male disloyal Customer 24 Business travel Eco
49783 dissatisfied Male disloyal Customer 39 Business travel Business
49788 dissatisfied Male disloyal Customer 25 Business travel Eco
49791 dissatisfied Male disloyal Customer 29 Business travel Eco
49794 dissatisfied Male disloyal Customer 27 Business travel Eco
49797 dissatisfied Male disloyal Customer 36 Business travel Business
49842 dissatisfied Male disloyal Customer 23 Business travel Business
49884 dissatisfied Female disloyal Customer 17 Business travel Eco
49891 dissatisfied Male disloyal Customer 22 Business travel Eco
49930 dissatisfied Male disloyal Customer 20 Business travel Eco Plus
49939 dissatisfied Male disloyal Customer 25 Business travel Eco
49966 dissatisfied Female disloyal Customer 25 Business travel Eco
49984 dissatisfied Male disloyal Customer 26 Business travel Business
49985 dissatisfied Male disloyal Customer 29 Business travel Eco
49994 dissatisfied Female disloyal Customer 25 Business travel Business
50004 dissatisfied Female disloyal Customer 25 Business travel Business
50011 dissatisfied Female disloyal Customer 25 Business travel Eco Plus
50046 dissatisfied Female disloyal Customer 37 Business travel Eco
50050 dissatisfied Male disloyal Customer 78 Business travel Eco
50055 dissatisfied Male disloyal Customer 16 Business travel Eco
50079 dissatisfied Female disloyal Customer 32 Business travel Eco
50088 dissatisfied Male disloyal Customer 22 Business travel Eco
50089 dissatisfied Female disloyal Customer 20 Business travel Eco
50123 dissatisfied Male disloyal Customer 27 Business travel Eco
50137 dissatisfied Female disloyal Customer 26 Business travel Business
50144 dissatisfied Male disloyal Customer 15 Business travel Eco
50148 dissatisfied Female disloyal Customer 30 Business travel Eco
50157 dissatisfied Male disloyal Customer 25 Business travel Business
50165 dissatisfied Female disloyal Customer 22 Business travel Eco
50184 dissatisfied Female disloyal Customer 23 Business travel Eco Plus
50198 dissatisfied Female disloyal Customer 22 Business travel Business
50221 dissatisfied Male disloyal Customer 36 Business travel Eco
50224 dissatisfied Female disloyal Customer 26 Business travel Eco
50235 dissatisfied Male disloyal Customer 49 Business travel Eco
50243 dissatisfied Male disloyal Customer 27 Business travel Eco
50278 dissatisfied Male disloyal Customer 11 Business travel Eco
50281 dissatisfied Male disloyal Customer 26 Business travel Business
50283 dissatisfied Female disloyal Customer 35 Business travel Eco
50306 dissatisfied Female disloyal Customer 18 Business travel Eco
50310 dissatisfied Male disloyal Customer 52 Business travel Eco
50319 dissatisfied Female disloyal Customer 39 Business travel Eco
50325 dissatisfied Female disloyal Customer 23 Business travel Eco
50351 dissatisfied Female disloyal Customer 39 Business travel Eco
50388 dissatisfied Female disloyal Customer 24 Business travel Eco
50389 dissatisfied Female disloyal Customer 25 Business travel Eco
50403 dissatisfied Female disloyal Customer 7 Business travel Eco
50426 dissatisfied Female disloyal Customer 26 Business travel Eco
50460 dissatisfied Female disloyal Customer 25 Business travel Business
50484 dissatisfied Male disloyal Customer 20 Business travel Eco
50486 dissatisfied Female disloyal Customer 42 Business travel Eco
50490 dissatisfied Male disloyal Customer 36 Business travel Eco
50503 dissatisfied Male disloyal Customer 37 Business travel Eco
50524 dissatisfied Female disloyal Customer 24 Business travel Eco
50549 dissatisfied Female disloyal Customer 51 Business travel Eco
50566 dissatisfied Female disloyal Customer 44 Business travel Eco
50572 dissatisfied Female disloyal Customer 35 Business travel Eco
50582 dissatisfied Male disloyal Customer 25 Business travel Eco
50624 dissatisfied Male disloyal Customer 54 Business travel Eco
50627 dissatisfied Female disloyal Customer 33 Business travel Eco
50647 dissatisfied Female disloyal Customer 27 Business travel Eco
50721 dissatisfied Male disloyal Customer 36 Business travel Eco
50723 dissatisfied Male disloyal Customer 25 Business travel Business
50743 dissatisfied Female disloyal Customer 52 Business travel Eco
50744 dissatisfied Male disloyal Customer 22 Business travel Eco
50755 dissatisfied Female disloyal Customer 42 Business travel Eco
50762 dissatisfied Female disloyal Customer 36 Business travel Eco
50763 dissatisfied Male disloyal Customer 47 Business travel Eco
50767 dissatisfied Female disloyal Customer 23 Business travel Eco
50771 dissatisfied Female disloyal Customer 17 Business travel Eco
50773 dissatisfied Male disloyal Customer 32 Business travel Eco
50774 dissatisfied Male disloyal Customer 21 Business travel Eco
50789 dissatisfied Female disloyal Customer 24 Business travel Eco
50796 dissatisfied Female disloyal Customer 79 Business travel Eco
50805 dissatisfied Male disloyal Customer 36 Business travel Eco
50826 dissatisfied Female disloyal Customer 24 Business travel Eco
50835 dissatisfied Male disloyal Customer 8 Business travel Business
50846 dissatisfied Female disloyal Customer 22 Business travel Eco
50857 dissatisfied Male disloyal Customer 20 Business travel Eco
50867 dissatisfied Female disloyal Customer 25 Business travel Business
50883 dissatisfied Male disloyal Customer 39 Business travel Eco
50884 dissatisfied Male disloyal Customer 25 Business travel Business
50891 dissatisfied Male disloyal Customer 24 Business travel Eco
50912 dissatisfied Male disloyal Customer 36 Business travel Eco
50919 dissatisfied Male disloyal Customer 25 Business travel Eco
50927 dissatisfied Female disloyal Customer 24 Business travel Eco
50976 dissatisfied Female disloyal Customer 50 Business travel Eco
51000 dissatisfied Male disloyal Customer 25 Business travel Business
51009 dissatisfied Male disloyal Customer 25 Business travel Business
51010 dissatisfied Female disloyal Customer 36 Business travel Eco
51030 dissatisfied Male disloyal Customer 30 Business travel Eco
51039 dissatisfied Female disloyal Customer 32 Business travel Eco
51048 dissatisfied Male disloyal Customer 41 Business travel Eco
51064 dissatisfied Female disloyal Customer 32 Business travel Eco
51072 dissatisfied Male disloyal Customer 24 Business travel Eco
51080 dissatisfied Male disloyal Customer 49 Business travel Eco
51086 dissatisfied Male disloyal Customer 35 Business travel Eco
51087 dissatisfied Female disloyal Customer 39 Business travel Eco
51093 dissatisfied Female disloyal Customer 37 Business travel Eco
51095 dissatisfied Male disloyal Customer 33 Business travel Eco
51109 dissatisfied Male disloyal Customer 21 Business travel Eco
51114 dissatisfied Male disloyal Customer 25 Business travel Eco
51123 dissatisfied Female disloyal Customer 25 Business travel Business
51142 dissatisfied Male disloyal Customer 36 Business travel Eco
51148 dissatisfied Male disloyal Customer 62 Business travel Eco
51180 dissatisfied Female disloyal Customer 22 Business travel Eco
51191 dissatisfied Male disloyal Customer 34 Business travel Eco
51193 dissatisfied Female disloyal Customer 22 Business travel Eco Plus
51207 dissatisfied Male disloyal Customer 21 Business travel Eco
51216 dissatisfied Male disloyal Customer 26 Business travel Eco
51228 dissatisfied Female disloyal Customer 37 Business travel Eco
51229 dissatisfied Female disloyal Customer 38 Business travel Eco
51234 dissatisfied Male disloyal Customer 17 Business travel Eco
51270 dissatisfied Female disloyal Customer 21 Business travel Eco
51273 dissatisfied Male disloyal Customer 18 Business travel Eco
51279 dissatisfied Male disloyal Customer 35 Business travel Eco
51292 dissatisfied Male disloyal Customer 25 Business travel Eco
51296 dissatisfied Male disloyal Customer 21 Business travel Eco
51299 dissatisfied Female disloyal Customer 35 Business travel Eco
51339 dissatisfied Female disloyal Customer 37 Business travel Eco
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51355 dissatisfied Female disloyal Customer 25 Business travel Eco
51360 dissatisfied Female disloyal Customer 24 Business travel Eco
51367 dissatisfied Male disloyal Customer 36 Business travel Eco
51373 dissatisfied Female disloyal Customer 62 Business travel Eco
51375 dissatisfied Female disloyal Customer 23 Business travel Eco
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51411 dissatisfied Male disloyal Customer 29 Business travel Eco
51424 dissatisfied Female disloyal Customer 21 Business travel Eco
51427 dissatisfied Male disloyal Customer 18 Business travel Eco
51432 dissatisfied Female disloyal Customer 36 Business travel Eco
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51444 dissatisfied Male disloyal Customer 28 Business travel Eco
51456 dissatisfied Male disloyal Customer 22 Business travel Eco
51459 dissatisfied Female disloyal Customer 23 Business travel Business
51489 dissatisfied Female disloyal Customer 21 Business travel Eco Plus
51503 dissatisfied Female disloyal Customer 44 Business travel Eco
51508 dissatisfied Male disloyal Customer 19 Business travel Eco
51510 dissatisfied Female disloyal Customer 24 Business travel Eco
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51536 dissatisfied Male disloyal Customer 47 Business travel Eco
51552 dissatisfied Male disloyal Customer 10 Business travel Eco
51555 dissatisfied Female disloyal Customer 36 Business travel Eco
51591 dissatisfied Female disloyal Customer 47 Business travel Eco Plus
51593 dissatisfied Male disloyal Customer 36 Business travel Eco
51597 dissatisfied Female disloyal Customer 40 Business travel Business
51600 dissatisfied Male disloyal Customer 39 Business travel Business
51604 dissatisfied Male disloyal Customer 36 Business travel Business
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51620 dissatisfied Male disloyal Customer 27 Business travel Business
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51645 dissatisfied Female disloyal Customer 48 Business travel Business
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51695 dissatisfied Male disloyal Customer 14 Business travel Eco
51737 dissatisfied Female disloyal Customer 24 Business travel Eco
51748 dissatisfied Female disloyal Customer 39 Business travel Business
51781 dissatisfied Male disloyal Customer 27 Business travel Business
51788 dissatisfied Male disloyal Customer 29 Business travel Eco
51796 dissatisfied Female disloyal Customer 15 Business travel Eco
51799 satisfied Female disloyal Customer 12 Business travel Eco
51828 dissatisfied Female disloyal Customer 25 Business travel Eco Plus
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51870 dissatisfied Female disloyal Customer 30 Business travel Eco
51889 dissatisfied Male disloyal Customer 43 Business travel Eco
51894 dissatisfied Male disloyal Customer 25 Business travel Eco
51898 dissatisfied Female disloyal Customer 36 Business travel Eco
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51998 dissatisfied Male disloyal Customer 18 Business travel Eco Plus
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52045 dissatisfied Female disloyal Customer 29 Business travel Eco
52049 dissatisfied Female disloyal Customer 25 Business travel Business
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52080 dissatisfied Male disloyal Customer 26 Business travel Eco
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52121 dissatisfied Female disloyal Customer 38 Business travel Eco
52128 dissatisfied Female disloyal Customer 25 Business travel Eco
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52158 dissatisfied Male disloyal Customer 17 Business travel Business
52162 dissatisfied Female disloyal Customer 38 Business travel Eco
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52170 dissatisfied Female disloyal Customer 30 Business travel Eco
52194 dissatisfied Male disloyal Customer 21 Business travel Eco
52200 dissatisfied Male disloyal Customer 36 Business travel Eco
52204 dissatisfied Female disloyal Customer 18 Business travel Eco Plus
52209 dissatisfied Female disloyal Customer 33 Business travel Eco
52210 dissatisfied Male disloyal Customer 26 Business travel Eco
52224 dissatisfied Female disloyal Customer 80 Business travel Business
52225 dissatisfied Female disloyal Customer 23 Business travel Eco
52227 dissatisfied Male disloyal Customer 69 Business travel Business
52236 dissatisfied Male disloyal Customer 61 Business travel Business
52239 dissatisfied Female disloyal Customer 59 Business travel Business
52240 dissatisfied Female disloyal Customer 35 Business travel Eco
52279 dissatisfied Female disloyal Customer 29 Business travel Eco
52291 dissatisfied Female disloyal Customer 44 Business travel Business
52307 satisfied Female disloyal Customer 42 Business travel Business
52341 dissatisfied Male disloyal Customer 24 Business travel Eco
52350 dissatisfied Female disloyal Customer 39 Business travel Business
52352 dissatisfied Female disloyal Customer 39 Business travel Business
52355 dissatisfied Female disloyal Customer 12 Business travel Eco
52357 dissatisfied Female disloyal Customer 35 Business travel Eco
52362 dissatisfied Female disloyal Customer 22 Business travel Eco
52374 dissatisfied Female disloyal Customer 39 Business travel Eco
52426 dissatisfied Female disloyal Customer 36 Business travel Business
52427 dissatisfied Female disloyal Customer 36 Business travel Business
52454 dissatisfied Female disloyal Customer 46 Business travel Eco
52459 dissatisfied Male disloyal Customer 34 Business travel Business
52474 dissatisfied Male disloyal Customer 34 Business travel Business
52484 dissatisfied Male disloyal Customer 23 Business travel Eco
52502 dissatisfied Male disloyal Customer 30 Business travel Eco Plus
52511 dissatisfied Female disloyal Customer 30 Business travel Eco Plus
52514 dissatisfied Male disloyal Customer 34 Business travel Eco
52516 dissatisfied Male disloyal Customer 29 Business travel Business
52529 dissatisfied Female disloyal Customer 29 Business travel Business
52542 dissatisfied Female disloyal Customer 24 Business travel Eco
52551 dissatisfied Male disloyal Customer 25 Business travel Eco
52559 dissatisfied Male disloyal Customer 27 Business travel Business
52583 dissatisfied Female disloyal Customer 26 Business travel Eco
52585 dissatisfied Male disloyal Customer 26 Business travel Business
52597 dissatisfied Male disloyal Customer 25 Business travel Eco
52598 dissatisfied Female disloyal Customer 26 Business travel Business
52613 dissatisfied Female disloyal Customer 23 Business travel Eco
52622 dissatisfied Female disloyal Customer 22 Business travel Eco
52625 dissatisfied Male disloyal Customer 21 Business travel Eco
52636 dissatisfied Male disloyal Customer 62 Business travel Business
52667 satisfied Female disloyal Customer 50 Business travel Business
52706 dissatisfied Female disloyal Customer 26 Business travel Eco
52707 satisfied Female disloyal Customer 45 Business travel Eco Plus
52721 dissatisfied Male disloyal Customer 44 Business travel Business
52735 dissatisfied Male disloyal Customer 23 Business travel Eco
52746 dissatisfied Female disloyal Customer 41 Business travel Business
52747 dissatisfied Female disloyal Customer 41 Business travel Business
52752 dissatisfied Female disloyal Customer 20 Business travel Eco
52761 dissatisfied Female disloyal Customer 40 Business travel Business
52786 dissatisfied Male disloyal Customer 25 Business travel Eco
52794 dissatisfied Female disloyal Customer 14 Business travel Eco
52796 satisfied Male disloyal Customer 38 Business travel Business
52798 dissatisfied Female disloyal Customer 38 Business travel Eco Plus
52845 dissatisfied Male disloyal Customer 28 Business travel Eco
52865 dissatisfied Male disloyal Customer 36 Business travel Business
52876 dissatisfied Male disloyal Customer 25 Business travel Eco
52881 dissatisfied Male disloyal Customer 25 Business travel Eco
52883 dissatisfied Female disloyal Customer 35 Business travel Eco
52904 dissatisfied Female disloyal Customer 35 Business travel Business
52910 dissatisfied Female disloyal Customer 35 Business travel Business
52911 dissatisfied Male disloyal Customer 62 Business travel Eco
52920 dissatisfied Male disloyal Customer 25 Business travel Eco
52922 dissatisfied Female disloyal Customer 34 Business travel Business
52928 dissatisfied Female disloyal Customer 34 Business travel Business
52976 dissatisfied Male disloyal Customer 22 Business travel Eco
52990 dissatisfied Female disloyal Customer 42 Business travel Eco
52992 dissatisfied Male disloyal Customer 22 Business travel Eco
53007 dissatisfied Male disloyal Customer 29 Business travel Business
53013 dissatisfied Male disloyal Customer 20 Business travel Eco
53018 dissatisfied Female disloyal Customer 28 Business travel Business
53028 dissatisfied Female disloyal Customer 23 Business travel Eco
53034 dissatisfied Male disloyal Customer 23 Business travel Eco
53040 dissatisfied Female disloyal Customer 27 Business travel Business
53044 dissatisfied Male disloyal Customer 33 Business travel Eco
53055 dissatisfied Female disloyal Customer 35 Business travel Eco
53073 dissatisfied Male disloyal Customer 26 Business travel Business
53079 dissatisfied Female disloyal Customer 26 Business travel Business
53101 dissatisfied Male disloyal Customer 24 Business travel Eco
53119 dissatisfied Male disloyal Customer 22 Business travel Eco
53130 dissatisfied Female disloyal Customer 28 Business travel Eco
53133 dissatisfied Female disloyal Customer 55 Business travel Eco
53138 dissatisfied Female disloyal Customer 21 Business travel Eco
53148 dissatisfied Male disloyal Customer 27 Business travel Eco
53160 dissatisfied Female disloyal Customer 62 Business travel Business
53172 dissatisfied Female disloyal Customer 13 Business travel Eco
53178 dissatisfied Female disloyal Customer 15 Business travel Eco
53181 dissatisfied Male disloyal Customer 34 Business travel Eco
53190 dissatisfied Male disloyal Customer 10 Business travel Eco
53209 dissatisfied Male disloyal Customer 56 Business travel Business
53221 dissatisfied Female disloyal Customer 10 Business travel Eco
53229 dissatisfied Female disloyal Customer 21 Business travel Eco
53231 dissatisfied Male disloyal Customer 23 Business travel Eco
53236 dissatisfied Male disloyal Customer 22 Business travel Eco
53237 dissatisfied Male disloyal Customer 21 Business travel Eco
53244 dissatisfied Female disloyal Customer 23 Business travel Eco
53249 dissatisfied Female disloyal Customer 52 Business travel Business
53251 dissatisfied Male disloyal Customer 31 Business travel Eco
53258 satisfied Female disloyal Customer 22 Business travel Eco
53271 dissatisfied Female disloyal Customer 8 Business travel Eco
53285 dissatisfied Male disloyal Customer 22 Business travel Eco
53288 dissatisfied Female disloyal Customer 49 Business travel Business
53316 satisfied Male disloyal Customer 47 Business travel Business
53367 dissatisfied Male disloyal Customer 45 Business travel Business
53368 dissatisfied Male disloyal Customer 35 Business travel Eco
53369 dissatisfied Female disloyal Customer 28 Business travel Eco
53397 dissatisfied Male disloyal Customer 44 Business travel Business
53414 dissatisfied Female disloyal Customer 43 Business travel Business
53432 satisfied Female disloyal Customer 42 Business travel Business
53447 dissatisfied Male disloyal Customer 42 Business travel Business
53460 dissatisfied Male disloyal Customer 41 Business travel Business
53469 dissatisfied Female disloyal Customer 41 Business travel Business
53488 dissatisfied Female disloyal Customer 40 Business travel Business
53524 dissatisfied Male disloyal Customer 39 Business travel Business
53535 dissatisfied Female disloyal Customer 39 Business travel Business
53547 dissatisfied Female disloyal Customer 39 Business travel Business
53566 dissatisfied Male disloyal Customer 39 Business travel Business
53571 dissatisfied Male disloyal Customer 39 Business travel Business
53588 satisfied Male disloyal Customer 38 Business travel Business
53598 dissatisfied Male disloyal Customer 21 Business travel Eco
53601 dissatisfied Female disloyal Customer 38 Business travel Business
53638 dissatisfied Female disloyal Customer 26 Business travel Eco
53651 dissatisfied Male disloyal Customer 37 Business travel Business
53695 satisfied Female disloyal Customer 37 Business travel Eco
53701 dissatisfied Male disloyal Customer 37 Business travel Business
53762 dissatisfied Female disloyal Customer 50 Business travel Eco
53777 dissatisfied Male disloyal Customer 20 Business travel Eco
53805 dissatisfied Female disloyal Customer 35 Business travel Business
53809 dissatisfied Female disloyal Customer 32 Business travel Eco
53815 dissatisfied Male disloyal Customer 30 Business travel Eco
53820 dissatisfied Female disloyal Customer 21 Business travel Eco
53831 dissatisfied Male disloyal Customer 35 Business travel Business
53835 dissatisfied Male disloyal Customer 34 Business travel Business
53844 dissatisfied Female disloyal Customer 27 Business travel Eco
53860 dissatisfied Male disloyal Customer 23 Business travel Eco
53866 dissatisfied Male disloyal Customer 34 Business travel Business
53871 dissatisfied Male disloyal Customer 34 Business travel Business
53872 dissatisfied Male disloyal Customer 34 Business travel Business
53876 dissatisfied Female disloyal Customer 24 Business travel Eco
53880 dissatisfied Male disloyal Customer 17 Business travel Eco
53881 dissatisfied Female disloyal Customer 33 Business travel Business
53932 dissatisfied Female disloyal Customer 31 Business travel Business
53938 dissatisfied Female disloyal Customer 26 Business travel Eco
53952 dissatisfied Male disloyal Customer 30 Business travel Business
53957 dissatisfied Female disloyal Customer 57 Business travel Eco
53970 dissatisfied Female disloyal Customer 30 Business travel Eco Plus
53974 dissatisfied Male disloyal Customer 47 Business travel Eco
53992 dissatisfied Male disloyal Customer 25 Business travel Eco
53999 dissatisfied Male disloyal Customer 29 Business travel Eco Plus
54009 dissatisfied Male disloyal Customer 29 Business travel Business
54011 dissatisfied Male disloyal Customer 29 Business travel Business
54027 dissatisfied Female disloyal Customer 29 Business travel Business
54030 dissatisfied Female disloyal Customer 29 Business travel Eco Plus
54039 dissatisfied Female disloyal Customer 29 Business travel Business
54046 dissatisfied Male disloyal Customer 27 Business travel Eco
54057 dissatisfied Male disloyal Customer 28 Business travel Eco Plus
54071 dissatisfied Female disloyal Customer 28 Business travel Eco Plus
54078 dissatisfied Female disloyal Customer 28 Business travel Business
54081 dissatisfied Female disloyal Customer 28 Business travel Business
54085 dissatisfied Male disloyal Customer 29 Business travel Eco
54109 dissatisfied Male disloyal Customer 27 Business travel Business
54110 dissatisfied Male disloyal Customer 27 Business travel Business
54140 dissatisfied Female disloyal Customer 19 Business travel Eco
54142 dissatisfied Female disloyal Customer 27 Business travel Business
54146 dissatisfied Female disloyal Customer 27 Business travel Business
54169 dissatisfied Male disloyal Customer 23 Business travel Eco
54190 dissatisfied Female disloyal Customer 30 Business travel Eco
54200 dissatisfied Female disloyal Customer 21 Business travel Eco
54210 dissatisfied Female disloyal Customer 26 Business travel Business
54213 dissatisfied Male disloyal Customer 23 Business travel Eco
54220 dissatisfied Male disloyal Customer 12 Business travel Eco
54225 dissatisfied Female disloyal Customer 28 Business travel Eco
54234 dissatisfied Female disloyal Customer 15 Business travel Eco
54262 dissatisfied Male disloyal Customer 21 Business travel Eco
54280 dissatisfied Female disloyal Customer 24 Business travel Business
54315 dissatisfied Female disloyal Customer 17 Business travel Eco
54316 dissatisfied Female disloyal Customer 33 Business travel Eco
54339 dissatisfied Female disloyal Customer 18 Business travel Eco
54342 dissatisfied Female disloyal Customer 39 Business travel Eco
54347 dissatisfied Female disloyal Customer 26 Business travel Eco
54373 dissatisfied Male disloyal Customer 36 Business travel Eco
54374 dissatisfied Female disloyal Customer 47 Business travel Eco
54395 dissatisfied Female disloyal Customer 80 Business travel Business
54417 dissatisfied Female disloyal Customer 61 Business travel Eco Plus
54418 dissatisfied Female disloyal Customer 42 Business travel Eco
54437 dissatisfied Male disloyal Customer 57 Business travel Business
54460 dissatisfied Female disloyal Customer 11 Business travel Eco
54480 dissatisfied Male disloyal Customer 29 Business travel Eco
54486 dissatisfied Male disloyal Customer 21 Business travel Eco
54488 dissatisfied Female disloyal Customer 25 Business travel Eco
54493 dissatisfied Female disloyal Customer 24 Business travel Eco
54509 dissatisfied Male disloyal Customer 34 Business travel Eco
54520 dissatisfied Male disloyal Customer 20 Business travel Eco
54523 dissatisfied Male disloyal Customer 48 Business travel Business
54526 dissatisfied Male disloyal Customer 16 Business travel Eco
54529 dissatisfied Female disloyal Customer 47 Business travel Business
54530 dissatisfied Male disloyal Customer 28 Business travel Eco
54544 dissatisfied Male disloyal Customer 46 Business travel Business
54573 satisfied Male disloyal Customer 44 Business travel Business
54591 dissatisfied Male disloyal Customer 44 Business travel Business
54594 dissatisfied Female disloyal Customer 22 Business travel Eco
54626 dissatisfied Male disloyal Customer 42 Business travel Business
54636 dissatisfied Male disloyal Customer 41 Business travel Business
54641 dissatisfied Female disloyal Customer 41 Business travel Business
54649 dissatisfied Male disloyal Customer 25 Business travel Eco
54656 dissatisfied Female disloyal Customer 40 Business travel Business
54684 satisfied Male disloyal Customer 39 Business travel Business
54701 dissatisfied Female disloyal Customer 39 Business travel Business
54704 dissatisfied Female disloyal Customer 39 Business travel Business
54709 dissatisfied Female disloyal Customer 39 Business travel Business
54718 dissatisfied Female disloyal Customer 29 Business travel Eco
54719 dissatisfied Female disloyal Customer 22 Business travel Eco
54720 dissatisfied Male disloyal Customer 39 Business travel Business
54749 dissatisfied Male disloyal Customer 22 Business travel Eco
54772 dissatisfied Male disloyal Customer 38 Business travel Business
54784 dissatisfied Male disloyal Customer 25 Business travel Eco
54803 dissatisfied Female disloyal Customer 20 Business travel Eco
54815 dissatisfied Male disloyal Customer 23 Business travel Eco
54820 dissatisfied Male disloyal Customer 20 Business travel Eco
54828 dissatisfied Female disloyal Customer 27 Business travel Eco
54852 satisfied Male disloyal Customer 37 Business travel Business
54863 dissatisfied Female disloyal Customer 36 Business travel Business
54886 dissatisfied Female disloyal Customer 60 Business travel Eco
54897 dissatisfied Male disloyal Customer 36 Business travel Business
54906 dissatisfied Male disloyal Customer 35 Business travel Business
54914 dissatisfied Male disloyal Customer 35 Business travel Eco Plus
54939 dissatisfied Male disloyal Customer 34 Business travel Business
54954 dissatisfied Male disloyal Customer 24 Business travel Eco
54963 dissatisfied Female disloyal Customer 64 Business travel Eco
54966 dissatisfied Female disloyal Customer 33 Business travel Business
55030 dissatisfied Female disloyal Customer 31 Business travel Business
55055 dissatisfied Female disloyal Customer 23 Business travel Eco
55080 dissatisfied Male disloyal Customer 29 Business travel Business
55082 dissatisfied Female disloyal Customer 30 Business travel Eco
55083 dissatisfied Male disloyal Customer 27 Business travel Eco
55094 dissatisfied Female disloyal Customer 29 Business travel Business
55157 dissatisfied Female disloyal Customer 28 Business travel Business
55170 dissatisfied Female disloyal Customer 21 Business travel Eco
55179 dissatisfied Female disloyal Customer 27 Business travel Business
55207 dissatisfied Male disloyal Customer 27 Business travel Business
55211 dissatisfied Male disloyal Customer 27 Business travel Business
55216 dissatisfied Male disloyal Customer 27 Business travel Business
55218 dissatisfied Female disloyal Customer 28 Business travel Eco
55225 dissatisfied Male disloyal Customer 27 Business travel Eco
55226 dissatisfied Male disloyal Customer 25 Business travel Eco
55238 dissatisfied Female disloyal Customer 26 Business travel Business
55261 dissatisfied Male disloyal Customer 22 Business travel Eco
55265 dissatisfied Female disloyal Customer 38 Business travel Eco
55269 dissatisfied Female disloyal Customer 23 Business travel Eco
55271 dissatisfied Male disloyal Customer 36 Business travel Eco
55272 dissatisfied Female disloyal Customer 22 Business travel Eco
55284 dissatisfied Male disloyal Customer 26 Business travel Eco
55293 dissatisfied Female disloyal Customer 20 Business travel Eco
55341 dissatisfied Female disloyal Customer 38 Business travel Eco
55344 dissatisfied Male disloyal Customer 21 Business travel Business
55362 dissatisfied Female disloyal Customer 42 Business travel Eco
55364 dissatisfied Male disloyal Customer 16 Business travel Eco
55368 dissatisfied Male disloyal Customer 23 Business travel Eco
55377 dissatisfied Male disloyal Customer 38 Business travel Eco
55378 dissatisfied Female disloyal Customer 22 Business travel Eco
55389 dissatisfied Male disloyal Customer 25 Business travel Eco
55395 dissatisfied Female disloyal Customer 24 Business travel Eco
55435 dissatisfied Female disloyal Customer 45 Business travel Business
55448 dissatisfied Female disloyal Customer 42 Business travel Business
55478 dissatisfied Female disloyal Customer 38 Business travel Business
55500 dissatisfied Female disloyal Customer 36 Business travel Business
55501 dissatisfied Female disloyal Customer 36 Business travel Business
55511 dissatisfied Male disloyal Customer 35 Business travel Business
55512 dissatisfied Male disloyal Customer 35 Business travel Business
55540 dissatisfied Male disloyal Customer 33 Business travel Business
55558 dissatisfied Male disloyal Customer 30 Business travel Eco Plus
55560 dissatisfied Male disloyal Customer 28 Business travel Eco
55562 dissatisfied Female disloyal Customer 25 Business travel Eco
55574 dissatisfied Female disloyal Customer 28 Business travel Business
55577 dissatisfied Male disloyal Customer 27 Business travel Business
55580 dissatisfied Male disloyal Customer 27 Business travel Business
55618 dissatisfied Female disloyal Customer 27 Business travel Eco
55628 dissatisfied Female disloyal Customer 30 Business travel Eco
55635 dissatisfied Female disloyal Customer 25 Business travel Business
55652 dissatisfied Male disloyal Customer 23 Business travel Eco
55687 dissatisfied Male disloyal Customer 25 Business travel Business
55748 dissatisfied Female disloyal Customer 26 Business travel Business
55756 dissatisfied Male disloyal Customer 26 Business travel Business
55775 dissatisfied Female disloyal Customer 25 Business travel Business
55791 dissatisfied Female disloyal Customer 25 Business travel Eco
55800 dissatisfied Female disloyal Customer 37 Business travel Eco
55815 dissatisfied Female disloyal Customer 35 Business travel Eco
55825 dissatisfied Male disloyal Customer 25 Business travel Eco
55829 dissatisfied Female disloyal Customer 32 Business travel Eco
55839 dissatisfied Male disloyal Customer 27 Business travel Eco
55841 dissatisfied Male disloyal Customer 15 Business travel Eco
55846 dissatisfied Female disloyal Customer 20 Business travel Eco
55882 dissatisfied Male disloyal Customer 20 Business travel Eco
55894 dissatisfied Male disloyal Customer 37 Business travel Eco
55923 dissatisfied Male disloyal Customer 57 Business travel Eco
55941 dissatisfied Male disloyal Customer 20 Business travel Eco
55946 dissatisfied Male disloyal Customer 21 Business travel Eco
55947 dissatisfied Male disloyal Customer 24 Business travel Eco
55982 dissatisfied Female disloyal Customer 34 Business travel Eco
55993 dissatisfied Male disloyal Customer 16 Business travel Eco
56024 dissatisfied Male disloyal Customer 18 Business travel Business
56028 dissatisfied Female disloyal Customer 26 Business travel Eco
56031 dissatisfied Female disloyal Customer 17 Business travel Eco
56034 dissatisfied Female disloyal Customer 47 Business travel Eco
56038 dissatisfied Male disloyal Customer 23 Business travel Eco
56053 dissatisfied Male disloyal Customer 21 Business travel Eco
56056 dissatisfied Female disloyal Customer 19 Business travel Eco
56061 dissatisfied Male disloyal Customer 43 Business travel Business
56070 dissatisfied Male disloyal Customer 38 Business travel Business
56077 dissatisfied Male disloyal Customer 25 Business travel Eco
56088 dissatisfied Male disloyal Customer 34 Business travel Eco
56096 dissatisfied Male disloyal Customer 24 Business travel Eco
56099 dissatisfied Female disloyal Customer 49 Business travel Business
56111 dissatisfied Female disloyal Customer 37 Business travel Business
56149 dissatisfied Male disloyal Customer 46 Business travel Business
56153 dissatisfied Male disloyal Customer 44 Business travel Business
56168 dissatisfied Male disloyal Customer 38 Business travel Business
56186 dissatisfied Male disloyal Customer 35 Business travel Business
56211 dissatisfied Female disloyal Customer 64 Business travel Eco
56219 dissatisfied Female disloyal Customer 30 Business travel Eco
56253 dissatisfied Male disloyal Customer 39 Business travel Business
56276 dissatisfied Female disloyal Customer 30 Business travel Business
56286 dissatisfied Male disloyal Customer 28 Business travel Business
56287 dissatisfied Female disloyal Customer 28 Business travel Eco
56289 dissatisfied Male disloyal Customer 27 Business travel Business
56303 dissatisfied Male disloyal Customer 36 Business travel Eco
56313 dissatisfied Male disloyal Customer 18 Business travel Eco
56317 dissatisfied Male disloyal Customer 38 Business travel Eco
56330 dissatisfied Male disloyal Customer 20 Business travel Eco
56334 dissatisfied Female disloyal Customer 16 Business travel Eco Plus
56335 dissatisfied Female disloyal Customer 26 Business travel Business
56351 dissatisfied Male disloyal Customer 25 Business travel Business
56355 dissatisfied Female disloyal Customer 25 Business travel Business
56358 dissatisfied Female disloyal Customer 25 Business travel Eco
56360 dissatisfied Male disloyal Customer 40 Business travel Eco
56365 dissatisfied Female disloyal Customer 34 Business travel Eco
56373 dissatisfied Male disloyal Customer 42 Business travel Eco
56387 dissatisfied Female disloyal Customer 25 Business travel Business
56391 dissatisfied Female disloyal Customer 25 Business travel Business
56424 dissatisfied Female disloyal Customer 26 Business travel Business
56428 dissatisfied Male disloyal Customer 22 Business travel Eco
56440 dissatisfied Female disloyal Customer 51 Business travel Eco
56443 dissatisfied Male disloyal Customer 24 Business travel Eco
56448 dissatisfied Male disloyal Customer 27 Business travel Eco
56466 dissatisfied Male disloyal Customer 28 Business travel Eco
56486 dissatisfied Female disloyal Customer 26 Business travel Business
56511 dissatisfied Male disloyal Customer 41 Business travel Eco
56512 dissatisfied Female disloyal Customer 21 Business travel Eco
56535 dissatisfied Male disloyal Customer 26 Business travel Business
56542 dissatisfied Male disloyal Customer 20 Business travel Eco
56557 dissatisfied Female disloyal Customer 27 Business travel Eco
56558 dissatisfied Female disloyal Customer 36 Business travel Eco
56565 dissatisfied Female disloyal Customer 26 Business travel Eco Plus
56576 dissatisfied Female disloyal Customer 22 Business travel Eco
56604 dissatisfied Female disloyal Customer 26 Business travel Business
56625 satisfied Male disloyal Customer 20 Business travel Eco
56635 satisfied Male disloyal Customer 26 Business travel Business
56656 satisfied Male disloyal Customer 26 Business travel Business
56699 satisfied Male disloyal Customer 24 Business travel Business
56706 satisfied Male disloyal Customer 23 Business travel Business
56713 satisfied Male disloyal Customer 23 Business travel Business
56715 satisfied Male disloyal Customer 22 Business travel Business
56737 satisfied Female disloyal Customer 21 Business travel Business
56750 satisfied Female disloyal Customer 21 Business travel Business
56767 dissatisfied Male disloyal Customer 23 Business travel Eco
56788 satisfied Male disloyal Customer 23 Business travel Eco
56796 satisfied Female disloyal Customer 18 Business travel Eco
56811 dissatisfied Female disloyal Customer 25 Business travel Eco
56835 dissatisfied Female disloyal Customer 12 Business travel Eco
56841 satisfied Male disloyal Customer 24 Business travel Business
56851 satisfied Female disloyal Customer 24 Business travel Business
56855 satisfied Male disloyal Customer 21 Business travel Eco
56889 satisfied Male disloyal Customer 22 Business travel Business
56898 satisfied Female disloyal Customer 22 Business travel Business
56905 satisfied Male disloyal Customer 22 Business travel Business
56921 satisfied Male disloyal Customer 27 Business travel Eco
56928 satisfied Male disloyal Customer 20 Business travel Business
56936 dissatisfied Male disloyal Customer 23 Business travel Eco
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49421 3 2 4 1
49429 3 3 3 1
49445 3 2 2 5
49446 3 4 3 3
49458 4 3 3 4
49460 3 1 3 5
49476 3 4 4 2
49501 3 3 3 4
49521 2 2 4 3
49527 2 3 1 2
49533 3 2 4 1
49552 4 1 4 1
49564 4 1 3 1
49579 1 5 3 3
49636 3 3 5 4
49638 3 4 4 5
49642 5 4 5 5
49648 1 4 5 2
49656 5 3 5 4
49666 5 4 4 1
49667 3 5 3 5
49680 4 5 3 5
49723 3 1 4 3
49727 4 1 3 3
49754 4 1 4 5
49760 3 1 4 2
49767 4 5 4 4
49772 4 4 3 3
49783 5 5 5 3
49788 5 3 5 3
49791 3 4 3 5
49794 2 5 3 3
49797 5 5 5 1
49842 3 4 3 1
49884 4 4 3 1
49891 5 4 5 4
49930 2 5 1 4
49939 2 1 1 1
49966 4 2 3 3
49984 4 5 4 5
49985 1 5 3 3
49994 5 3 5 4
50004 5 4 4 1
50011 3 5 1 5
50046 4 2 3 3
50050 4 2 3 3
50055 3 4 3 3
50079 3 2 4 2
50088 1 2 3 3
50089 4 3 4 3
50123 3 2 4 5
50137 5 5 5 4
50144 4 4 4 1
50148 3 3 4 3
50157 5 4 5 3
50165 4 3 4 3
50184 3 1 3 3
50198 4 4 3 2
50221 1 3 1 5
50224 4 3 4 4
50235 4 1 4 1
50243 2 4 1 5
50278 5 3 5 1
50281 5 5 5 5
50283 4 1 4 5
50306 4 2 4 4
50310 3 2 4 5
50319 5 4 4 2
50325 4 5 2 2
50351 2 4 1 2
50388 5 5 5 4
50389 2 2 3 2
50403 5 5 4 2
50426 3 2 2 2
50460 4 5 5 1
50484 5 3 4 2
50486 3 5 5 4
50490 4 2 3 4
50503 2 2 2 2
50524 5 5 5 5
50549 5 4 3 3
50566 1 1 2 3
50572 5 1 3 5
50582 4 2 3 1
50624 5 3 5 3
50627 5 1 1 2
50647 3 4 5 4
50721 4 3 2 2
50723 5 5 4 1
50743 4 1 4 1
50744 4 3 3 4
50755 2 4 4 4
50762 2 1 4 5
50763 2 1 3 1
50767 4 5 5 2
50771 5 1 1 1
50773 1 5 1 1
50774 4 4 5 2
50789 1 5 3 1
50796 5 1 1 3
50805 1 2 1 1
50826 4 3 5 3
50835 3 3 4 2
50846 4 4 5 4
50857 1 5 1 5
50867 4 3 4 5
50883 3 2 5 1
50884 5 4 5 2
50891 4 5 5 1
50912 4 2 3 5
50919 2 5 4 4
50927 4 4 5 5
50976 4 3 2 3
51000 5 5 3 5
51009 5 5 5 3
51010 3 1 3 3
51030 2 5 1 3
51039 5 4 4 3
51048 2 1 5 1
51064 5 1 5 4
51072 3 2 5 3
51080 1 2 5 4
51086 2 1 2 1
51087 1 5 3 1
51093 2 1 1 4
51095 4 5 4 1
51109 5 2 3 4
51114 4 5 3 3
51123 5 3 4 1
51142 1 5 1 3
51148 5 5 1 3
51180 5 5 5 3
51191 1 1 5 3
51193 4 3 5 1
51207 4 3 4 2
51216 3 2 2 5
51228 2 3 2 4
51229 3 2 3 5
51234 3 3 3 3
51270 2 4 2 2
51273 3 1 3 1
51279 3 3 2 5
51292 2 4 3 4
51296 3 3 2 4
51299 3 2 3 4
51339 3 1 4 5
51353 3 1 4 3
51355 3 3 4 5
51360 3 3 3 5
51367 4 3 4 3
51373 3 1 3 5
51375 2 1 2 4
51397 4 4 4 2
51411 4 2 4 2
51424 4 1 3 3
51427 3 1 3 4
51432 4 4 4 4
51433 4 3 3 2
51444 3 4 4 2
51456 3 2 2 2
51459 3 2 4 1
51489 3 2 3 5
51503 3 1 4 4
51508 3 1 3 2
51510 4 3 4 5
51512 4 3 4 2
51515 4 4 3 2
51526 4 1 3 5
51527 4 2 4 5
51536 3 1 4 3
51552 3 2 4 3
51555 3 4 4 3
51591 4 5 2 4
51593 1 4 2 1
51597 5 2 1 5
51600 3 1 2 2
51604 5 4 4 2
51606 5 5 4 2
51612 5 3 5 2
51620 2 2 3 5
51630 3 3 3 2
51645 4 5 5 5
51676 3 4 2 1
51695 4 4 1 4
51737 4 4 3 4
51748 5 5 5 2
51781 4 1 3 4
51788 2 4 5 5
51796 3 1 4 4
51799 4 4 5 5
51828 2 1 2 3
51857 2 4 3 4
51870 4 4 2 4
51889 3 2 3 1
51894 4 2 3 3
51898 4 3 3 3
51905 3 1 3 4
51931 4 3 4 3
51937 4 4 4 2
51949 4 4 3 3
51958 2 4 2 3
51989 3 4 3 3
51998 3 1 4 5
52003 4 3 3 1
52006 3 1 4 5
52015 4 1 4 1
52023 3 3 3 2
52035 4 2 4 5
52040 3 4 3 5
52044 3 3 3 1
52045 4 1 3 1
52049 3 4 4 4
52050 3 2 3 2
52058 4 1 4 5
52074 3 2 4 4
52080 4 3 4 3
52081 4 2 4 1
52093 3 3 4 2
52121 4 4 3 3
52128 3 2 3 4
52146 4 3 4 4
52158 4 4 4 5
52162 3 3 3 1
52163 3 1 4 3
52170 3 3 4 5
52194 4 3 3 2
52200 3 4 4 5
52204 3 3 2 1
52209 3 3 3 3
52210 1 1 5 1
52224 3 3 3 2
52225 5 3 3 3
52227 5 4 4 2
52236 5 5 4 3
52239 5 5 5 1
52240 2 4 5 4
52279 3 4 1 4
52291 5 4 4 5
52307 5 3 4 5
52341 5 4 4 3
52350 4 4 4 4
52352 5 2 3 5
52355 3 5 4 2
52357 2 4 2 2
52362 5 3 5 3
52374 5 3 5 4
52426 5 5 4 4
52427 4 4 4 2
52454 2 1 1 5
52459 5 3 3 3
52474 4 4 5 4
52484 1 2 5 5
52502 1 2 4 5
52511 3 2 2 5
52514 2 1 5 3
52516 4 4 5 5
52529 5 4 4 3
52542 3 4 5 1
52551 2 3 2 3
52559 4 4 5 4
52583 3 5 5 3
52585 2 4 1 1
52597 5 2 2 1
52598 5 3 5 2
52613 2 2 5 2
52622 5 3 1 5
52625 2 3 3 3
52636 2 3 2 4
52667 4 3 4 2
52706 2 3 3 5
52707 5 4 5 1
52721 3 1 3 2
52735 4 5 5 3
52746 5 5 4 5
52747 4 4 5 3
52752 3 2 2 4
52761 3 3 3 5
52786 4 3 4 1
52794 4 5 4 3
52796 4 4 5 5
52798 1 3 5 3
52845 2 3 3 4
52865 5 5 4 5
52876 2 1 3 2
52881 3 3 2 4
52883 2 5 4 5
52904 5 5 4 4
52910 5 4 4 3
52911 5 2 5 1
52920 3 5 5 4
52922 4 3 5 1
52928 4 3 5 4
52976 4 5 5 2
52990 1 3 3 4
52992 2 4 3 1
53007 2 2 3 1
53013 1 1 2 5
53018 4 3 4 5
53028 5 4 5 2
53034 2 3 2 3
53040 5 3 4 1
53044 2 1 2 1
53055 3 5 1 1
53073 5 4 5 4
53079 4 5 5 1
53101 5 4 3 3
53119 2 2 3 4
53130 3 4 4 4
53133 5 1 1 3
53138 2 3 4 3
53148 3 2 3 5
53160 3 3 4 2
53172 2 1 3 4
53178 2 2 3 2
53181 3 5 1 5
53190 4 1 3 5
53209 2 1 2 2
53221 4 3 4 5
53229 3 1 4 1
53231 3 3 3 1
53236 3 4 3 1
53237 4 1 4 5
53244 4 4 4 4
53249 5 5 4 3
53251 4 4 3 1
53258 4 4 5 1
53271 4 1 4 1
53285 2 2 4 5
53288 4 3 4 2
53316 4 5 4 3
53367 3 5 2 4
53368 2 1 3 2
53369 3 3 3 1
53397 5 3 4 2
53414 4 3 4 5
53432 4 3 4 3
53447 4 5 5 5
53460 5 3 5 2
53469 3 2 1 4
53488 5 3 2 3
53524 5 3 5 2
53535 5 4 4 4
53547 4 1 4 1
53566 5 4 5 1
53571 3 4 3 3
53588 5 3 5 4
53598 4 5 5 2
53601 5 3 5 3
53638 3 3 4 4
53651 4 5 2 5
53695 4 5 3 3
53701 4 3 3 4
53762 3 4 4 3
53777 4 3 5 3
53805 5 3 4 1
53809 3 2 3 4
53815 3 1 3 1
53820 5 4 4 2
53831 4 5 5 1
53835 3 5 1 5
53844 3 5 3 3
53860 3 4 4 2
53866 4 3 5 5
53871 4 3 4 2
53872 5 4 5 1
53876 5 5 4 2
53880 3 4 3 5
53881 4 5 5 1
53932 4 4 5 3
53938 3 3 4 1
53952 5 4 4 2
53957 3 1 2 4
53970 3 3 3 5
53974 2 4 2 3
53992 3 2 2 5
53999 3 4 4 4
54009 5 5 5 5
54011 5 5 5 4
54027 5 5 4 1
54030 4 1 3 1
54039 5 4 5 4
54046 3 3 4 2
54057 2 3 2 2
54071 3 1 2 5
54078 4 4 4 1
54081 4 5 5 5
54085 3 5 2 5
54109 3 2 1 3
54110 5 4 5 1
54140 1 4 5 3
54142 4 3 4 1
54146 4 5 4 2
54169 4 4 4 3
54190 3 4 2 1
54200 3 4 4 5
54210 3 2 3 3
54213 3 5 2 5
54220 4 1 3 5
54225 3 1 3 2
54234 3 3 1 3
54262 2 3 2 5
54280 3 1 3 2
54315 3 1 4 4
54316 4 3 3 1
54339 4 4 3 2
54342 4 3 4 2
54347 3 1 3 2
54373 3 2 4 2
54374 2 5 5 5
54395 2 2 2 3
54417 2 1 3 3
54418 3 4 2 2
54437 4 3 5 5
54460 4 4 3 1
54480 3 3 4 3
54486 4 3 5 4
54488 5 3 4 3
54493 3 3 3 5
54509 4 1 1 2
54520 4 3 3 2
54523 4 3 3 2
54526 4 3 3 1
54529 4 4 4 5
54530 2 4 3 5
54544 5 4 5 3
54573 5 3 5 2
54591 3 1 3 1
54594 3 2 3 3
54626 4 3 4 4
54636 4 2 4 2
54641 4 1 3 4
54649 3 4 3 3
54656 4 4 3 1
54684 5 4 5 4
54701 3 1 3 4
54704 4 1 3 1
54709 4 1 4 3
54718 5 5 5 2
54719 3 4 4 4
54720 5 4 4 3
54749 4 3 5 4
54772 3 2 3 2
54784 4 3 4 2
54803 4 4 3 1
54815 3 1 3 4
54820 4 4 3 2
54828 3 4 2 1
54852 5 2 5 3
54863 4 3 5 3
54886 3 2 4 2
54897 4 3 5 1
54906 5 5 4 3
54914 4 3 4 2
54939 4 4 4 5
54954 3 4 4 2
54963 4 2 4 5
54966 5 4 5 2
55030 3 3 5 5
55055 4 1 3 3
55080 4 3 5 3
55082 3 2 4 3
55083 3 2 4 4
55094 4 5 5 1
55157 5 4 4 2
55170 3 4 3 1
55179 5 3 4 1
55207 4 3 4 5
55211 1 1 3 4
55216 3 2 3 3
55218 3 4 4 2
55225 4 2 4 2
55226 3 3 3 2
55238 4 1 3 5
55261 4 1 3 3
55265 4 2 4 5
55269 4 3 5 1
55271 3 3 3 3
55272 3 1 3 3
55284 2 2 1 5
55293 4 4 3 1
55341 3 1 4 1
55344 4 4 1 4
55362 4 3 3 3
55364 3 3 3 5
55368 3 1 1 1
55377 4 5 3 5
55378 4 3 3 4
55389 1 2 3 3
55395 3 1 4 3
55435 4 5 5 4
55448 4 5 4 1
55478 5 4 5 3
55500 5 3 5 2
55501 4 5 4 1
55511 5 4 4 1
55512 4 3 4 5
55540 5 4 5 4
55558 1 3 3 5
55560 3 5 5 1
55562 4 4 4 2
55574 4 3 4 2
55577 5 4 4 2
55580 4 5 4 5
55618 3 4 4 3
55628 3 2 4 1
55635 5 5 4 3
55652 5 3 4 3
55687 4 5 4 3
55748 4 5 4 1
55756 4 3 4 1
55775 5 3 5 2
55791 3 4 4 1
55800 3 1 3 4
55815 4 3 3 1
55825 3 4 3 1
55829 2 5 3 4
55839 4 2 4 5
55841 3 2 3 2
55846 3 4 4 3
55882 4 3 2 5
55894 5 3 4 5
55923 4 3 3 2
55941 1 5 2 4
55946 4 3 5 2
55947 3 4 3 4
55982 3 1 4 1
55993 4 3 3 3
56024 4 2 3 4
56028 3 2 3 5
56031 3 1 4 3
56034 4 1 4 4
56038 3 3 3 4
56053 5 1 4 2
56056 3 4 2 1
56061 5 5 4 1
56070 5 2 3 2
56077 5 3 4 1
56088 1 3 2 2
56096 4 5 4 2
56099 5 5 4 2
56111 5 5 5 1
56149 4 4 5 2
56153 5 3 5 4
56168 5 5 4 5
56186 4 3 4 1
56211 4 1 4 3
56219 3 3 4 2
56253 4 3 5 5
56276 3 1 1 1
56286 5 4 5 1
56287 4 4 3 1
56289 5 4 5 5
56303 4 4 4 2
56313 3 3 4 5
56317 1 3 3 1
56330 5 5 3 5
56334 5 2 4 1
56335 4 4 4 5
56351 4 4 5 2
56355 4 5 5 4
56358 3 5 1 3
56360 2 5 2 3
56365 1 5 5 3
56373 5 1 1 4
56387 3 5 4 1
56391 5 5 4 5
56424 4 3 5 1
56428 4 5 5 5
56440 3 4 1 5
56443 5 5 4 3
56448 3 4 1 3
56466 3 1 4 2
56486 5 4 5 3
56511 5 1 5 5
56512 5 2 3 2
56535 4 5 5 4
56542 4 5 5 3
56557 3 3 1 3
56558 4 1 2 2
56565 1 3 1 4
56576 5 2 5 5
56604 4 5 5 2
56625 5 4 4 2
56635 5 4 5 4
56656 4 5 5 4
56699 5 5 5 1
56706 4 3 5 1
56713 4 3 5 5
56715 4 5 4 2
56737 5 4 4 4
56750 5 3 5 3
56767 3 3 3 1
56788 4 1 5 4
56796 4 3 5 4
56811 2 5 4 5
56835 5 5 5 4
56841 5 3 4 5
56851 2 2 4 4
56855 3 2 5 2
56889 5 5 5 2
56898 5 5 5 1
56905 5 4 5 5
56921 4 1 2 5
56928 5 5 4 5
56936 5 3 5 4
Departure.Delay.in.Minutes Arrival.Delay.in.Minutes
23 0 0
30 0 0
34 0 0
37 0 0
50 0 0
64 0 0
87 0 3
92 0 0
94 0 2
115 0 0
125 0 3
134 0 0
135 0 0
160 0 3
167 1 0
169 0 0
178 0 0
185 0 0
192 0 0
213 0 9
234 64 50
246 0 1
251 1 0
276 5 0
278 0 0
283 0 0
288 0 0
293 4 0
307 0 2
313 0 0
344 3 3
352 32 0
389 0 5
404 10 0
412 0 0
420 127 131
428 0 0
434 2 0
454 0 0
456 0 0
478 0 0
498 0 0
511 0 0
514 0 0
533 0 0
543 0 0
545 10 6
581 0 0
592 1 1
599 0 3
611 0 0
612 0 0
620 14 5
636 1 0
643 3 0
652 0 0
688 0 0
703 0 0
709 0 0
747 3 0
758 0 0
762 0 0
764 0 0
769 11 2
773 0 0
782 4 0
807 0 0
835 0 0
854 21 12
880 0 3
885 293 302
890 122 124
903 0 0
910 5 2
915 0 6
916 2 0
920 0 0
923 0 0
929 0 0
962 46 56
995 62 63
1004 0 0
1044 3 15
1058 0 0
1061 5 0
1064 5 0
1067 10 0
1072 201 208
1076 0 0
1092 0 0
1094 0 0
1096 0 0
1114 0 0
1146 0 2
1156 10 18
1178 0 0
1179 0 0
1186 0 0
1188 24 37
1193 18 0
1213 0 0
1214 0 0
1225 0 0
1229 30 21
1240 0 0
1246 0 0
1249 0 0
1264 0 0
1340 22 17
1351 0 0
1387 2 12
1396 45 29
1397 0 0
1404 0 0
1443 3 0
1450 0 0
1455 40 46
1471 0 0
1475 26 29
1479 0 0
1489 0 0
1499 0 0
1506 15 0
1529 0 0
1559 0 0
1564 0 0
1576 1 0
1614 0 25
1627 0 0
1636 0 5
1660 1 0
1661 0 0
1668 0 37
1685 0 4
1696 24 39
1704 1 0
1722 0 0
1732 4 0
1760 0 0
1761 0 0
1769 39 14
1776 0 35
1802 22 11
1810 0 0
1812 0 0
1819 0 0
1843 8 0
1849 0 0
1866 0 0
1899 0 0
1901 6 1
1907 0 0
1934 0 0
1950 0 0
1961 2 23
1968 14 12
1982 0 0
2001 74 91
2008 25 24
2015 0 0
2030 0 0
2056 2 0
2069 7 0
2086 0 0
2100 111 133
2112 16 NA
2115 0 4
2126 1 0
2138 0 0
2139 58 49
2173 0 9
2188 0 0
2207 0 19
2209 0 0
2224 24 28
2246 64 64
2249 0 0
2287 0 0
2292 0 5
2295 19 19
2297 0 0
2317 0 0
2372 29 28
2387 108 117
2401 13 28
2408 34 44
2411 52 71
2416 0 0
2442 145 164
2456 24 12
2464 0 0
2516 0 0
2531 34 26
2551 0 0
2553 0 0
2555 0 10
2564 0 0
2583 0 0
2598 11 0
2599 5 0
2606 118 103
2648 0 0
2655 0 24
2668 2 10
2713 0 6
2717 0 0
2732 0 0
2739 0 0
2756 0 0
2760 5 40
2764 73 48
2767 0 0
2774 0 0
2792 0 0
2795 0 0
2813 0 0
2815 0 0
2841 0 0
2865 0 7
2874 0 0
2875 0 0
2892 0 0
2908 2 0
2910 1 0
2917 36 22
2933 0 0
2938 7 21
2941 0 14
2945 18 1
2946 0 0
2967 0 0
2993 22 15
3004 1 0
3007 72 62
3023 38 29
3026 0 0
3029 2 0
3035 46 67
3037 0 4
3055 15 33
3057 0 0
3069 0 0
3077 0 0
3080 11 0
3082 2 0
3095 91 82
3121 0 0
3128 0 1
3133 27 4
3150 4 7
3153 13 15
3156 32 31
3167 21 13
3184 0 0
3186 0 1
3199 0 0
3202 2 0
3220 70 75
3248 52 48
3251 0 0
3254 79 82
3259 0 0
3262 20 12
3292 0 0
3297 0 0
3305 0 0
3310 13 0
3318 14 18
3319 1 0
3331 22 16
3332 0 0
3365 0 0
3368 26 21
3394 0 0
3406 0 4
3434 0 0
3444 5 0
3454 0 0
3459 6 3
3471 142 135
3480 7 19
3507 3 0
3515 0 0
3542 0 33
3551 78 67
3552 0 14
3580 0 0
3612 0 0
3629 8 5
3630 0 0
3663 4 0
3668 1 0
3670 10 0
3690 0 0
3691 5 13
3711 0 0
3732 17 0
3746 0 0
3747 6 0
3754 1 1
3761 0 0
3808 32 15
3811 26 13
3820 0 0
3834 11 3
3841 39 54
3853 1 13
3864 0 0
3871 13 11
3880 243 256
3882 0 0
3904 0 0
3911 5 0
3933 102 87
3948 0 1
3954 0 0
3972 136 NA
3975 6 0
3988 0 0
3993 2 0
4000 0 0
4010 0 0
4027 0 0
4043 3 9
4051 2 0
4053 10 4
4055 2 0
4080 1 9
4082 0 0
4090 0 0
4091 17 0
4116 0 0
4208 0 9
4209 105 83
4210 47 49
4233 0 0
4244 0 0
4257 0 0
4260 2 2
4262 0 0
4264 0 0
4318 0 0
4335 15 9
4341 47 59
4354 36 48
4362 31 0
4393 0 0
4394 3 0
4402 62 49
4416 0 7
4439 4 0
4440 95 88
4442 0 0
4458 25 40
4488 44 16
4492 7 0
4500 0 0
4503 0 0
4582 2 0
4586 24 20
4598 0 0
4601 0 0
4605 0 0
4608 24 16
4617 0 0
4627 0 8
4655 0 0
4658 0 0
4683 31 11
4686 8 3
4694 0 0
4697 0 0
4722 43 44
4752 27 25
4756 0 10
4762 0 2
4783 27 13
4802 0 0
4806 81 64
4813 34 28
4832 0 0
4862 111 158
4870 0 2
4876 2 0
4879 0 0
4928 12 0
4938 1 3
4965 0 0
4981 0 0
4983 0 0
5010 0 0
5044 84 110
5046 0 10
5057 0 1
5075 36 52
5086 0 0
5119 15 6
5125 0 0
5148 41 44
5154 5 0
5162 8 13
5169 0 6
5180 21 20
5224 0 4
5266 1 0
5295 1 2
5326 0 0
5330 0 0
5331 32 33
5341 1 0
5382 31 10
5387 25 24
5403 98 99
5433 0 0
5438 0 0
5452 15 18
5464 0 0
5477 134 121
5478 0 5
5486 0 0
5497 29 13
5523 0 0
5533 0 0
5543 0 0
5549 62 36
5551 1 0
5556 0 0
5577 1 9
5588 62 83
5589 14 3
5622 7 137
5627 0 10
5654 0 2
5662 3 0
5675 91 92
5693 2 0
5694 4 2
5699 0 0
5710 0 0
5712 0 0
5716 70 66
5734 0 0
5739 0 0
5758 0 1
5760 0 0
5762 64 49
5788 0 0
5797 108 106
5829 0 0
5833 0 0
5837 0 0
5847 0 0
5888 43 0
5915 33 24
5917 0 0
5931 0 0
5945 0 0
5949 15 3
5953 106 71
5977 0 0
5984 24 0
5989 51 39
5998 0 0
5999 0 3
6002 1 20
6018 0 0
6019 34 36
6027 0 6
6071 36 70
6081 0 0
6097 0 0
6101 4 9
6112 0 0
6127 9 6
6146 0 0
6156 0 0
6159 4 12
6160 66 50
6182 3 8
6186 75 73
6189 0 0
6200 0 0
6202 0 0
6214 0 0
6216 1 3
6217 0 0
6231 0 1
6239 3 2
6257 17 15
6267 0 0
6269 0 3
6274 0 0
6280 0 0
6287 0 0
6309 44 35
6360 0 0
6365 0 0
6382 0 0
6393 35 37
6416 0 0
6452 0 0
6461 3 1
6523 0 14
6529 0 0
6540 0 0
6541 0 0
6547 16 0
6552 0 0
6588 0 0
6591 8 3
6625 8 7
6636 27 37
6665 0 0
6667 47 54
6684 5 2
6689 0 0
6691 3 2
6707 0 0
6725 8 18
6728 0 NA
6745 12 8
6752 2 0
6753 0 0
6758 14 0
6766 0 0
6780 0 0
6790 0 0
6810 0 0
6815 19 0
6825 0 7
6842 45 NA
6843 0 0
6854 19 4
6871 0 0
6874 0 0
6879 12 5
6880 0 0
6891 5 0
6892 13 0
6918 62 52
6938 43 19
6940 0 0
6955 0 7
6972 0 0
6974 0 0
6983 0 0
6993 84 83
7004 0 0
7008 0 0
7034 0 2
7065 0 11
7069 0 0
7070 53 69
7072 20 14
7082 0 0
7090 8 0
7152 3 0
7154 0 0
7172 0 1
7179 0 0
7187 0 0
7198 0 8
7202 67 96
7204 0 0
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7431 0 2
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11005 210 196
11014 88 78
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13366 4 1
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15268 0 0
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16368 10 31
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16416 16 7
16450 0 0
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[ reached 'max' / getOption("max.print") -- omitted 5533 rows ]
summary(data)
satisfaction Gender Customer.Type Age
Length:9880 Length:9880 Length:9880 Min. : 7.00
Class :character Class :character Class :character 1st Qu.:27.00
Mode :character Mode :character Mode :character Median :39.00
Mean :39.26
3rd Qu.:51.00
Max. :85.00
Type.of.Travel Class Flight.Distance Seat.comfort
Length:9880 Length:9880 Min. : 50 Min. :0.000
Class :character Class :character 1st Qu.:1370 1st Qu.:2.000
Mode :character Mode :character Median :1939 Median :3.000
Mean :1999 Mean :2.838
3rd Qu.:2560 3rd Qu.:4.000
Max. :6907 Max. :5.000
Departure.Arrival.time.convenient Food.and.drink Gate.location
Min. :0.000 Min. :0.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
Median :3.000 Median :3.000 Median :3.000
Mean :2.988 Mean :2.845 Mean :2.991
3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
Max. :5.000 Max. :5.000 Max. :5.000
Inflight.wifi.service Inflight.entertainment Online.support
Min. :0.000 Min. :0.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
Median :3.000 Median :4.000 Median :4.000
Mean :3.247 Mean :3.385 Mean :3.533
3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000
Max. :5.000 Max. :5.000 Max. :5.000
Ease.of.Online.booking On.board.service Leg.room.service Baggage.handling
Min. :0.000 Min. :0.00 Min. :0.000 Min. :1.000
1st Qu.:2.000 1st Qu.:3.00 1st Qu.:2.000 1st Qu.:3.000
Median :4.000 Median :4.00 Median :4.000 Median :4.000
Mean :3.477 Mean :3.46 Mean :3.484 Mean :3.689
3rd Qu.:5.000 3rd Qu.:4.00 3rd Qu.:5.000 3rd Qu.:5.000
Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
Checkin.service Cleanliness Online.boarding Departure.Delay.in.Minutes
Min. :1.000 Min. :0.000 Min. :0.000 Min. : 0.00
1st Qu.:3.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.: 0.00
Median :3.000 Median :4.000 Median :4.000 Median : 0.00
Mean :3.341 Mean :3.698 Mean :3.361 Mean : 14.73
3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.: 12.00
Max. :5.000 Max. :5.000 Max. :5.000 Max. :815.00
Arrival.Delay.in.Minutes
Min. : 0.00
1st Qu.: 0.00
Median : 0.00
Mean : 15.12
3rd Qu.: 13.00
Max. :822.00
NA's :26
Graph representations:¶
ggplot(data, aes(x = Customer.Type, fill = as.factor(satisfaction))) +
geom_bar(position = "fill") +
labs(fill = "Satisfaction", title= "Incident of Satisfaction between customer type")
The bar graph depicts the relationship between customer satisfaction and customer type. Our research reveals that loyal customers generally display higher levels of satisfaction in comparison to their disloyal counterparts.
histglucose <- hist(data$Seat.comfort,xlim=c(0,5),
main="Histogram of Avg. seat comfort with Normal Distribution Overlay", xlab="Avg. seat comfort",las=1)
xfit <- seq(min(data$Seat.comfort),max(data$Seat.comfort))
yfit <- dnorm(xfit,mean=mean(data$Seat.comfort),sd=sd(data$Seat.comfort))
yfit <- yfit*diff(histglucose$mids[1:2])*length(data$Seat.comfort)
lines(xfit,yfit,col="red",lwd=2)
This histogram shows us the average of the seat comfort rating, and from the graph above we can see it's a left skewed, this show us the mean in this column is typically less than the median. Also, we can see the frequency start to increase when the rating reach 3.
tab <- data$Class %>% table()
precentages <- tab %>% prop.table() %>% round(3) * 100
txt <- paste0(names(tab), '\n', precentages, '%') # text on chart
colors <- c("royalblue4", "mediumorchid", "lavender")
pie(tab, labels=txt ,col = colors, main= "Chart of Class Type") # plot pie chart
This pie chart displays the distribution of passenger classes in our dataset, which is categorized as nominal data. The data reveals that Business Class passengers constitute the highest proportion at 47.8%, followed by Economy Class passengers at 45.1%, while the smallest percentage corresponds to Eco Plus Class passengers, accounting for 7.2% of the total.
install.packages("scatterplot3d")
library(scatterplot3d)
scatterplot3d(data$Cleanliness, data$Class, data$Food.and.drink)
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
a scatter plot consists of individual data points.
4- Data preprocessing¶
Data preprocessing is a critical step in the machine learning workflow, essential for optimizing model performance and ensuring reliable predictions. Common scenarios demanding preprocessing include addressing missing values by imputation or removal, handling outliers to prevent them from skewing model behavior, normalizing or scaling features to bring them to a consistent scale, encoding categorical variables for numerical compatibility, and engineering new features to enhance model understanding.
4.1 -Data Cleaning :¶
Missing data can introduce challenges during data analysis or the construction of machine learning models since they have the potential to yield inaccurate results or generate errors. In our dataset, we proactively identified and addressed missing data by opting to replace those missing values with the mean.
#looking for missing data and datatypes for all feature
sum(is.na(data))
#looking for missing data and datatypes for each feature
sapply(data, function(x) sum(is.na(x)))
- satisfaction
- 0
- Gender
- 0
- Customer.Type
- 0
- Age
- 0
- Type.of.Travel
- 0
- Class
- 0
- Flight.Distance
- 0
- Seat.comfort
- 0
- Departure.Arrival.time.convenient
- 0
- Food.and.drink
- 0
- Gate.location
- 0
- Inflight.wifi.service
- 0
- Inflight.entertainment
- 0
- Online.support
- 0
- Ease.of.Online.booking
- 0
- On.board.service
- 0
- Leg.room.service
- 0
- Baggage.handling
- 0
- Checkin.service
- 0
- Cleanliness
- 0
- Online.boarding
- 0
- Departure.Delay.in.Minutes
- 0
- Arrival.Delay.in.Minutes
- 26
#handle the missing value by replacing it with the mean
data$Arrival.Delay.in.Minutes<- ifelse(
is.na(data$Arrival.Delay.in.Minutes),
round(mean(data$Arrival.Delay.in.Minutes, na.rm = TRUE)),
data$Arrival.Delay.in.Minutes
)
#the result after we handle it
sum(is.na(data$Arrival.Delay.in.Minutes))
4.2 -Remove outliers :¶
We looked at our data and found some unusual values that don't fit in well with the rest. These odd values, called outliers, So, we needed to get rid of them before we started working on our project.
To do that, we used a tool called the Outliers package, which has a function called Outlier(). It helped us find and highlight the outlier data points in our dataset.
#find the outlier in Arrival Delay in Minutes
OutAM <- outlier(data$Arrival.Delay.in.Minutes)
print(OutAM)
[1] 822
#Remove Arrival.Delay.in.Minutes outlier
data <- data[data$Arrival.Delay.in.Minutes != OutAM, ]
#find the outlier in Flight Distance
OutFD <- outlier(data$Flight.Distance)
print(OutFD)
[1] 6907
#Remove Flight.Distance outlier
data <- data[data$Flight.Distance!= OutFD, ]
#find the outlier in Departure Delay in Minutes
OutDM <- outlier(data$Departure.Delay.in.Minutes)
print(OutDM)
[1] 581
#Remove Departure.Delay.in.Minutes outlier
data <- data[data$Departure.Delay.in.Minutes!= OutDM, ]
#find the outlier in Age
OutAG <- outlier(data$Age)
print(OutAG)
[1] 85
#Remove Age outlier
data <- data[data$Age!= OutAG, ]
4.3 - Data Transformation¶
4.3.1-Encoding¶
Encoding is crucial in data mining and machine learning because it changes raw data into a format algorithms can understand. This often means turning categories or words into numbers, making it easier for computers to work with and analyze the information.
# Replace 'Gender' column with 1(female) or 2(male)
data$Gender <- as.integer(factor(data$Gender, levels = unique(data$Gender)))
# Replace 'Customer Type' column with 1(loyal Customer) or 2(disloyal Customer)
data$Customer.Type<- as.integer(factor(data$Customer.Type, levels = unique(data$Customer.Type)))
# Replace 'Type of Travel' column with 1(Personal Travel) or 2(Business travel)
data$Type.of.Travel<- as.integer(factor(data$Type.of.Travel, levels = unique(data$Type.of.Travel)))
# Replace 'Class' column with 1(Eco), 2(Business) or 3(Eco Plus)
data$Class <- as.integer(factor(data$Class, levels = unique(data$Class)))
# Define a dictionary to manually recode 'satisfied' to 1 and 'dissatisfied' to 0
dictionary <- c('dissatisfied' = 0, 'satisfied' = 1)
# Replace values in 'Satisfaction' column using the dictionary
data <- data %>% mutate(satisfaction = recode(satisfaction, !!!dictionary))
head(data)
| satisfaction | Gender | Customer.Type | Age | Type.of.Travel | Class | Flight.Distance | Seat.comfort | Departure.Arrival.time.convenient | Food.and.drink | ⋯ | Online.support | Ease.of.Online.booking | On.board.service | Leg.room.service | Baggage.handling | Checkin.service | Cleanliness | Online.boarding | Departure.Delay.in.Minutes | Arrival.Delay.in.Minutes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | ⋯ | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <dbl> | |
| 23 | 1 | 1 | 1 | 25 | 1 | 1 | 2122 | 0 | 1 | 0 | ⋯ | 4 | 2 | 4 | 1 | 3 | 1 | 3 | 2 | 0 | 0 |
| 30 | 1 | 1 | 1 | 35 | 1 | 1 | 3695 | 0 | 1 | 0 | ⋯ | 4 | 2 | 2 | 3 | 4 | 4 | 3 | 4 | 0 | 0 |
| 34 | 1 | 1 | 1 | 26 | 1 | 1 | 2408 | 0 | 1 | 0 | ⋯ | 4 | 4 | 1 | 4 | 4 | 2 | 3 | 4 | 0 | 0 |
| 37 | 1 | 1 | 1 | 10 | 1 | 1 | 3209 | 0 | 1 | 0 | ⋯ | 4 | 4 | 4 | 3 | 3 | 1 | 4 | 4 | 0 | 0 |
| 50 | 1 | 2 | 1 | 34 | 1 | 1 | 1816 | 0 | 1 | 0 | ⋯ | 4 | 4 | 1 | 1 | 2 | 3 | 2 | 4 | 0 | 0 |
| 64 | 1 | 1 | 1 | 11 | 1 | 1 | 1761 | 0 | 1 | 0 | ⋯ | 3 | 3 | 1 | 2 | 3 | 2 | 3 | 3 | 0 | 0 |
4.3.2 - Discretization¶
We have categorized flight distances into four finite elements: 0, 1400, 2800, 4200, 7000 and, inf. Into the showing labels 1 as short , 2 as medium, 3 as moderate, 4 as long , and 5 as very long ,This allows for detailed analysis and insights into various aspects of air travel.
#Assuming 'data' is your data frame and 'Flight.Distance' is the column to be discretized
# Define the bin edges and labels
bin.edges <- c(0, 1400, 2800, 4200, 7000, Inf) # Modify these bins as needed
bin.labels <- c(1, 2, 3, 4, 5)
# Perform discretization using cut() function
data$Flight.Distance<- cut(data$Flight.Distance, breaks = bin.edges, labels = bin.labels)
# Now, the 'Flight Distance' column has been discretized into categories and stored in 'Flight.Distance.Category
print(data$Flight.Distance)
[1] 2 3 2 3 2 2 2 3 2 2 2 1 2 2 2 2 2 1 1 2 2 2 3 2 2 2 1 1 2 3 1 3 2 1 2 2 2 [38] 1 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 1 3 2 1 1 1 2 1 3 2 2 1 2 3 3 1 1 2 2 1 1 [75] 2 3 2 2 2 1 2 1 2 2 2 3 3 3 2 2 2 2 1 1 2 3 2 3 2 2 1 2 2 2 2 1 2 1 1 1 1 [112] 1 2 1 2 2 2 1 1 2 2 2 2 1 3 2 2 2 2 2 2 2 2 2 1 2 1 2 3 3 3 2 2 3 1 2 2 2 [149] 1 2 2 2 2 2 2 2 2 2 1 1 2 3 2 2 3 1 2 2 2 2 2 2 1 2 2 1 1 2 2 1 1 1 3 2 2 [186] 2 1 2 2 1 1 2 2 2 1 1 2 2 3 3 3 2 2 2 2 1 1 2 2 2 1 2 2 2 1 1 1 2 2 3 3 2 [223] 2 3 1 2 2 2 1 2 2 2 2 1 2 1 1 2 2 2 1 3 2 1 2 2 3 2 2 1 1 2 3 2 1 2 3 1 2 [260] 2 1 2 1 2 1 2 2 2 1 2 1 2 3 2 2 2 1 1 2 2 1 2 2 1 2 2 2 1 2 1 1 1 1 2 2 2 [297] 2 3 3 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 1 2 2 2 1 2 1 1 2 1 1 2 1 2 3 3 3 1 [334] 3 1 1 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 1 1 2 2 2 2 1 1 2 1 1 1 2 1 2 1 2 1 2 [371] 2 1 1 1 2 1 3 2 3 2 2 3 1 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 2 2 1 2 1 2 1 1 2 [408] 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 3 2 2 1 2 2 2 2 2 1 2 1 1 2 2 2 1 2 1 2 1 2 [445] 2 2 2 2 1 1 1 1 2 2 2 2 2 3 2 3 2 2 2 1 2 1 1 2 1 2 2 2 2 1 2 1 2 1 2 1 2 [482] 1 2 2 2 2 1 1 1 2 2 2 2 2 3 2 2 2 1 2 2 2 1 2 1 2 2 2 2 2 2 2 1 1 1 2 1 1 [519] 3 2 3 3 3 2 2 2 2 1 3 2 2 1 2 2 2 2 1 2 1 2 2 2 2 1 2 2 2 2 2 1 1 2 2 2 2 [556] 2 1 1 1 2 1 2 2 1 1 2 2 3 2 2 2 2 2 1 1 1 2 1 2 1 1 1 1 1 1 2 1 2 2 2 3 2 [593] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 1 1 2 2 2 2 2 2 2 1 2 3 1 2 1 2 1 2 2 2 2 2 [630] 2 2 1 1 2 2 3 2 2 2 2 2 1 2 2 2 1 1 2 1 2 3 2 1 3 3 2 2 2 2 2 2 2 2 2 1 2 [667] 1 1 1 3 2 3 2 3 2 2 2 1 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 1 2 [704] 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 1 1 1 1 1 2 2 1 1 2 1 3 1 2 1 1 2 2 2 2 1 2 [741] 2 2 2 3 2 1 2 2 2 1 1 2 1 2 3 2 2 3 3 2 1 1 2 1 2 2 2 2 1 2 2 2 1 2 1 2 2 [778] 2 1 1 2 2 1 2 1 1 2 2 3 3 2 2 2 2 2 1 2 2 2 2 2 1 1 1 1 2 3 2 2 1 2 2 2 1 [815] 2 1 1 3 1 2 2 2 2 2 2 1 2 2 2 3 3 2 2 2 2 1 2 2 2 2 2 1 1 4 2 2 3 3 3 2 3 [852] 3 2 2 2 1 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 3 2 3 3 1 2 2 2 2 2 [889] 2 2 2 2 2 1 2 2 1 2 1 2 1 2 2 2 3 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 1 1 2 2 2 [926] 1 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 1 2 1 2 1 1 3 2 2 2 2 2 2 2 1 2 2 2 2 2 [963] 1 2 2 2 2 2 2 2 1 1 2 2 1 2 2 1 2 2 1 2 2 2 2 1 2 1 2 1 2 1 1 1 1 3 3 4 2 [1000] 2 2 2 2 3 1 2 1 2 1 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 2 1 2 2 1 2 2 2 [1037] 1 1 1 2 2 2 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 [1074] 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 1 2 2 1 2 2 2 2 2 1 1 1 2 1 1 2 2 2 1 2 2 1 [1111] 1 1 2 1 1 3 3 3 3 3 3 2 2 3 2 3 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 [1148] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 1 2 2 2 2 2 1 2 2 2 1 2 3 3 [1185] 2 2 2 2 2 2 2 2 1 2 2 1 2 1 1 2 1 1 1 3 3 2 2 2 2 4 1 1 2 2 1 2 1 2 2 2 2 [1222] 1 1 1 2 1 1 2 2 2 1 1 2 1 2 2 2 2 3 2 1 1 1 2 2 1 3 2 3 2 3 2 1 3 2 2 2 2 [1259] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 1 1 2 2 2 2 1 2 2 2 3 [1296] 3 3 3 3 3 2 2 3 2 3 1 2 2 1 2 1 3 2 2 2 1 2 2 1 1 2 2 1 2 2 1 2 2 2 2 1 2 [1333] 2 2 2 2 1 2 1 2 1 2 2 1 1 2 2 2 1 3 3 2 3 3 2 2 2 3 2 2 2 2 2 1 1 2 2 2 2 [1370] 1 1 2 2 1 2 1 1 1 1 2 2 2 2 2 2 1 2 2 1 2 2 2 1 2 2 1 2 2 1 2 1 2 1 2 2 1 [1407] 2 2 1 2 2 2 2 2 1 1 1 2 2 1 1 3 3 2 3 3 2 3 3 3 3 2 3 1 2 2 1 1 2 1 2 2 2 [1444] 2 1 2 2 2 2 1 2 2 2 2 1 2 2 1 1 2 1 1 1 1 1 1 2 2 1 2 3 3 2 2 2 2 2 2 2 2 [1481] 2 1 2 2 1 2 2 2 1 2 2 2 1 2 2 2 1 2 1 2 1 2 1 2 2 2 1 1 1 2 2 2 1 1 2 2 2 [1518] 2 3 2 2 2 2 3 3 1 2 2 3 2 2 1 1 1 1 2 1 2 1 1 1 3 1 2 2 2 2 1 2 3 3 1 2 2 [1555] 2 1 2 1 1 2 1 2 2 1 4 4 2 3 3 2 1 2 2 1 1 1 1 2 2 2 2 1 1 1 2 2 2 1 2 1 2 [1592] 1 3 3 2 2 2 1 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 1 1 2 2 2 [1629] 3 2 2 1 2 2 1 1 3 3 3 3 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 [1666] 2 1 1 2 2 2 3 2 2 2 2 1 1 2 2 2 2 2 2 2 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 3 3 [1703] 2 1 1 1 1 1 1 2 2 2 1 2 1 1 1 2 2 1 2 2 1 2 1 2 2 1 2 2 1 3 2 1 2 1 1 1 2 [1740] 1 2 2 2 2 1 2 2 3 3 3 2 1 3 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 1 [1777] 1 2 1 2 2 2 3 2 2 3 1 2 2 2 1 2 1 2 1 2 2 1 1 1 2 2 2 2 2 2 2 1 2 1 1 2 2 [1814] 2 2 3 1 2 3 2 2 1 1 1 1 2 1 3 2 2 2 2 2 2 2 2 2 2 2 2 4 3 2 3 3 3 2 2 2 2 [1851] 1 2 2 1 2 3 2 1 2 2 2 2 2 1 2 2 2 1 2 2 1 2 2 2 2 2 1 1 1 1 1 1 2 2 1 1 2 [1888] 2 1 1 2 2 2 1 1 2 1 3 3 3 3 3 2 2 2 2 2 2 3 2 1 2 2 2 2 2 2 2 2 1 1 2 2 1 [1925] 2 1 1 2 1 2 2 2 2 1 1 2 1 3 3 3 3 3 3 3 3 3 3 2 3 3 1 2 2 2 2 1 1 1 2 1 2 [1962] 2 2 2 2 2 2 1 2 2 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 2 1 2 2 2 1 2 2 1 1 1 1 2 [1999] 2 2 2 2 1 2 1 2 2 2 1 2 1 2 1 1 1 1 2 2 1 1 1 2 2 2 3 3 3 3 3 2 2 3 3 2 2 [2036] 2 3 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 1 1 1 1 2 2 1 1 2 2 2 2 [2073] 2 1 2 2 2 1 1 2 1 3 3 3 3 2 2 2 2 2 3 1 2 2 2 2 1 2 1 1 2 2 2 2 2 1 2 2 1 [2110] 1 2 2 2 2 2 2 2 2 1 2 2 1 2 1 3 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 1 2 3 2 2 2 [2147] 1 2 2 2 2 2 2 2 3 2 3 1 3 2 3 3 3 3 3 3 2 3 2 3 2 1 1 2 1 1 2 2 1 2 2 2 2 [2184] 2 2 2 1 2 1 2 2 1 2 1 2 1 2 1 2 1 2 1 1 2 3 2 3 2 2 2 1 1 1 3 2 2 1 2 2 2 [2221] 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 2 2 2 1 1 1 2 2 2 2 1 3 3 3 3 3 [2258] 2 2 2 2 3 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 2 1 1 1 2 1 [2295] 1 1 2 1 2 2 1 1 2 1 2 1 2 1 2 1 2 1 1 2 2 1 2 2 1 2 2 2 2 2 1 3 3 3 2 3 3 [2332] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 1 1 1 1 2 1 2 2 1 2 2 2 2 1 1 1 [2369] 1 1 1 2 2 2 2 2 1 2 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 1 1 3 3 2 2 2 3 2 2 2 1 [2406] 1 2 1 1 1 2 4 1 2 2 2 2 1 2 2 2 3 2 2 2 3 1 1 2 2 2 2 3 1 2 2 2 2 1 1 2 2 [2443] 2 1 2 3 2 2 2 2 2 1 2 1 1 1 1 2 2 2 2 1 2 2 1 1 3 2 2 2 2 1 1 2 2 1 1 3 3 [2480] 3 3 2 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 3 1 1 1 3 2 1 2 2 2 3 2 1 1 1 [2517] 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 3 2 3 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 1 [2554] 1 2 3 1 1 2 1 1 2 2 3 1 2 2 2 3 2 1 2 1 2 1 2 2 3 2 2 2 3 2 2 3 2 2 2 2 2 [2591] 2 2 2 2 1 1 1 3 3 3 3 3 3 1 1 2 2 2 2 2 2 2 1 2 2 2 2 1 2 1 1 3 2 3 2 2 1 [2628] 2 3 2 1 2 1 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 2 2 1 1 1 2 1 2 2 1 2 2 1 [2665] 1 2 4 3 3 3 3 2 2 3 2 3 2 2 1 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 [2702] 2 2 1 2 1 3 1 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 2 2 1 [2739] 2 2 2 2 3 3 2 2 2 2 2 3 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 1 1 1 1 4 3 3 3 2 [2776] 3 3 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 1 2 3 3 3 2 3 2 2 2 1 2 2 2 1 2 2 2 2 [2813] 2 2 2 2 1 2 2 1 2 1 2 1 1 2 2 2 1 3 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 [2850] 1 1 2 1 1 2 2 2 1 2 1 3 2 2 3 2 2 2 1 2 2 1 2 1 1 1 1 2 3 2 3 1 2 2 2 1 1 [2887] 2 2 2 3 2 2 2 2 1 2 2 1 1 1 1 2 1 2 1 1 2 2 1 2 1 3 1 2 1 1 2 1 2 2 1 3 2 [2924] 2 2 2 2 2 2 1 2 3 3 2 2 2 2 1 1 2 1 2 2 2 2 2 1 2 1 1 1 2 2 2 2 2 2 1 1 2 [2961] 2 2 2 1 3 3 3 2 2 1 1 1 2 3 3 1 2 2 1 2 1 3 3 2 1 1 2 2 1 2 2 2 2 2 2 3 3 [2998] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 [3035] 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 1 1 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 2 [3072] 2 2 2 2 2 2 1 2 2 2 2 1 1 1 1 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 [3109] 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 1 2 2 2 2 3 3 2 2 3 2 2 2 3 2 2 2 2 4 2 [3146] 2 2 1 2 2 2 2 4 3 2 2 4 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 [3183] 2 2 2 2 2 2 2 3 2 2 2 2 2 2 1 2 2 2 2 2 2 3 2 1 1 2 2 4 2 2 2 2 2 3 2 1 2 [3220] 3 1 2 2 2 3 2 2 2 2 1 2 3 2 3 2 1 1 2 1 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 [3257] 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 1 2 2 2 2 1 3 2 1 3 2 [3294] 2 2 2 2 2 2 2 3 2 1 2 2 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 1 [3331] 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 [3368] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 [3405] 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 1 2 2 [3442] 2 2 2 1 2 2 1 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 2 3 2 2 2 2 2 [3479] 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 3 2 3 2 2 3 2 2 3 2 2 2 2 2 2 3 2 2 2 2 [3516] 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 2 2 2 3 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 [3553] 2 2 2 2 3 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 3 3 2 1 2 2 2 2 2 2 2 [3590] 2 2 3 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 [3627] 2 2 2 2 2 1 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 1 3 2 3 2 3 2 3 3 3 2 2 2 1 2 2 [3664] 2 2 2 1 2 1 2 2 2 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3701] 2 2 2 1 2 2 1 2 1 2 2 2 1 2 3 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2 2 1 2 2 1 2 2 [3738] 2 3 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 3 2 3 2 2 2 3 3 2 2 2 2 3 2 2 2 2 3 2 [3775] 3 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 1 2 2 1 2 2 2 2 1 2 [3812] 2 2 2 2 2 2 1 2 1 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 2 2 1 1 3 2 2 2 2 [3849] 2 2 2 2 2 1 2 1 2 2 2 2 1 2 2 1 2 1 1 1 2 1 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 [3886] 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 2 3 2 1 2 3 2 2 4 1 [3923] 2 2 2 3 2 2 2 2 3 3 2 2 2 2 1 1 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [3960] 2 1 2 2 1 1 2 2 2 2 2 2 1 2 2 1 2 3 2 2 2 2 2 2 2 2 2 2 2 1 2 3 2 2 2 1 2 [3997] 3 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 1 [4034] 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 3 3 2 2 2 2 3 2 2 3 3 3 3 3 2 3 2 2 2 2 [4071] 2 2 2 2 1 2 2 2 3 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 [4108] 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 [4145] 2 2 2 2 2 2 1 1 3 2 3 3 3 2 2 2 2 2 3 2 2 2 2 2 2 2 4 1 3 2 2 2 2 2 2 2 2 [4182] 2 1 2 2 2 2 2 1 3 2 2 3 2 3 2 2 2 3 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 [4219] 2 2 2 2 2 1 2 2 2 2 1 1 1 3 1 2 2 2 2 1 2 2 2 2 2 2 1 2 3 2 2 2 2 2 2 2 2 [4256] 2 2 2 2 2 1 1 1 2 2 2 2 1 2 1 2 1 2 2 2 3 3 2 2 2 2 3 3 2 2 3 2 2 2 2 3 2 [4293] 3 1 1 2 2 2 2 2 2 2 1 2 2 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 2 [4330] 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 2 3 3 2 2 2 2 2 2 2 2 2 2 3 2 2 1 1 1 2 3 [4367] 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 3 3 2 1 2 2 2 3 2 1 2 3 2 2 1 2 [4404] 2 2 2 2 1 2 3 1 2 2 2 1 1 2 2 2 2 1 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [4441] 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 3 2 2 2 2 3 2 3 2 2 2 3 1 2 [4478] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 1 2 2 2 2 2 2 2 2 2 2 3 2 3 2 [4515] 1 3 2 2 3 3 2 2 3 2 2 2 3 2 2 2 3 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 [4552] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 2 2 1 2 2 2 2 2 2 1 3 2 2 2 1 2 [4589] 2 2 3 2 2 1 3 2 2 2 2 2 3 3 3 2 2 2 2 3 3 2 2 1 2 2 2 2 2 2 2 2 3 2 2 2 3 [4626] 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 1 [4663] 2 2 1 2 1 1 2 2 2 3 2 2 2 2 2 3 2 1 3 2 2 2 2 1 2 1 2 1 2 2 2 2 1 2 1 2 3 [4700] 2 2 2 2 2 1 1 2 2 2 3 2 2 2 2 2 2 1 2 2 2 3 2 2 1 2 1 2 2 1 2 2 3 2 1 1 2 [4737] 1 2 2 2 2 1 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 1 2 1 [4774] 2 2 2 2 2 2 2 2 2 2 2 2 4 1 4 2 2 2 3 3 2 2 3 2 1 3 2 2 2 3 2 2 2 3 2 2 1 [4811] 2 2 2 2 2 2 2 2 2 2 2 3 2 2 3 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 2 [4848] 2 1 3 2 2 3 2 2 3 2 2 3 2 2 1 3 1 2 2 3 2 2 1 1 2 2 3 1 1 3 2 1 1 1 2 2 3 [4885] 3 3 3 2 2 1 2 3 1 1 1 1 2 1 3 1 2 2 3 2 1 3 3 2 3 2 1 2 3 1 2 2 1 1 3 3 1 [4922] 1 3 2 1 1 2 1 1 2 1 3 2 4 1 3 3 3 1 3 3 3 3 1 3 2 1 4 2 3 3 2 3 2 3 1 3 1 [4959] 1 2 1 3 2 1 2 3 3 2 2 2 2 2 2 2 2 2 3 2 3 4 3 2 2 2 3 1 2 2 1 3 2 2 3 3 1 [4996] 1 1 3 2 1 1 1 2 2 2 3 3 2 1 3 2 3 3 2 1 3 3 1 2 1 2 2 2 2 1 2 3 1 2 1 1 3 [5033] 1 1 1 2 3 2 3 2 1 1 2 3 2 2 2 2 2 2 1 1 2 2 2 3 3 1 1 2 2 3 2 2 1 1 2 1 1 [5070] 2 1 1 2 1 3 2 2 3 3 2 3 2 2 2 2 1 1 3 1 3 1 3 3 3 4 3 1 2 3 2 1 3 2 3 1 1 [5107] 1 2 3 1 1 1 1 4 1 4 2 2 2 3 1 1 3 1 2 1 1 2 3 2 2 1 1 1 1 2 2 1 1 2 3 2 2 [5144] 3 2 1 1 3 2 2 2 2 2 2 3 2 4 1 3 2 2 2 3 2 2 2 2 2 2 2 2 1 2 1 2 1 1 2 3 2 [5181] 3 1 2 3 1 3 2 2 1 2 1 2 2 2 2 1 3 4 4 3 1 1 2 4 1 2 1 2 2 1 3 2 3 2 2 3 3 [5218] 3 1 2 2 2 2 2 2 1 3 1 1 2 2 1 3 2 2 2 3 2 1 1 2 1 3 4 2 1 1 3 2 2 2 3 3 1 [5255] 1 4 3 2 2 2 1 4 1 2 2 2 1 2 2 3 1 1 1 2 2 2 2 3 1 3 3 2 2 2 3 2 3 2 3 2 2 [5292] 2 2 2 4 2 1 1 4 2 2 1 1 1 3 1 4 2 2 1 1 1 4 2 2 2 1 3 2 2 1 1 2 2 1 3 3 2 [5329] 3 2 2 3 2 2 2 2 2 1 3 2 1 2 1 1 2 2 2 2 3 2 2 2 1 3 2 1 3 1 1 1 3 1 3 2 1 [5366] 1 1 2 4 2 1 1 1 1 3 1 1 1 3 1 1 2 2 3 3 3 2 2 2 1 3 2 2 2 2 2 1 2 3 3 2 2 [5403] 2 3 2 2 3 3 2 3 1 3 1 2 2 2 2 1 2 1 2 3 2 2 1 2 2 1 1 3 2 2 2 3 2 3 2 2 2 [5440] 3 1 1 1 1 3 3 1 3 1 3 2 3 2 3 3 1 2 2 2 1 2 1 3 2 1 2 1 2 2 3 2 1 2 1 2 1 [5477] 2 1 2 3 1 2 2 2 3 2 2 2 2 3 2 2 2 3 1 1 3 2 2 1 1 2 2 2 2 2 3 2 2 3 1 2 2 [5514] 2 3 2 2 2 2 1 1 1 1 1 2 2 4 4 2 1 1 3 2 3 2 2 2 2 1 1 1 2 2 2 1 1 3 1 1 3 [5551] 1 1 1 3 3 1 2 2 1 2 1 3 2 3 1 2 2 1 2 2 2 1 1 2 1 1 1 3 2 2 3 2 1 3 3 3 1 [5588] 2 2 1 2 2 1 1 1 1 3 2 2 1 1 2 2 3 4 3 1 1 4 1 2 3 2 2 2 2 2 2 2 3 1 3 2 3 [5625] 1 1 1 4 3 3 2 3 2 1 2 3 2 2 2 1 1 2 2 1 4 3 2 3 1 1 2 2 2 2 1 2 4 2 1 1 3 [5662] 2 2 2 2 2 2 1 2 2 2 2 2 1 3 1 3 3 2 2 2 2 3 2 2 2 2 3 2 2 3 3 2 1 3 3 2 2 [5699] 4 3 1 2 1 4 2 3 2 2 2 1 1 2 2 1 2 3 2 1 2 2 2 1 3 2 2 3 2 3 3 2 1 2 2 1 2 [5736] 2 2 3 1 3 3 2 2 2 1 3 1 2 1 2 3 1 1 3 2 2 1 1 1 1 2 3 1 1 3 2 3 3 1 2 1 3 [5773] 2 2 2 4 1 1 1 3 1 1 2 2 2 1 2 2 2 3 2 2 1 3 3 2 1 4 3 3 2 1 1 1 2 3 4 2 2 [5810] 1 3 1 2 1 1 1 2 2 3 2 2 1 1 2 2 2 2 2 2 2 2 2 3 3 2 1 4 3 2 1 2 1 2 1 3 2 [5847] 2 2 3 2 2 2 2 3 2 1 2 3 4 1 2 2 1 2 4 1 2 2 2 2 1 1 2 3 2 3 1 2 2 1 2 2 2 [5884] 3 3 2 3 1 1 2 2 2 2 1 2 2 3 3 3 1 3 2 2 1 2 1 2 2 1 2 2 2 1 1 2 2 1 2 3 2 [5921] 2 1 2 2 1 2 1 2 2 2 4 3 1 1 2 2 3 1 1 3 2 1 1 2 3 1 4 2 2 2 2 3 1 1 1 2 2 [5958] 3 2 2 2 1 3 2 2 1 2 2 3 2 3 1 3 2 1 2 3 2 3 1 3 1 1 3 2 1 3 2 1 1 1 2 2 1 [5995] 1 4 1 1 4 2 2 3 1 1 2 2 3 2 2 1 1 2 1 3 1 2 2 2 3 1 3 2 2 2 2 1 3 2 1 2 1 [6032] 4 2 2 2 1 2 2 2 2 2 2 2 1 4 3 3 2 1 2 1 1 3 1 1 2 2 4 2 2 1 3 1 3 3 4 3 3 [6069] 3 2 1 1 2 1 4 2 3 1 2 2 2 2 2 3 2 2 1 1 3 1 3 1 2 2 1 1 1 2 2 1 2 1 2 3 2 [6106] 1 3 1 3 2 1 1 2 3 1 1 2 2 2 2 4 1 3 3 2 2 2 1 1 2 2 1 1 2 1 2 3 3 1 2 1 1 [6143] 1 2 3 2 2 3 2 2 2 1 2 4 1 1 2 2 1 2 3 1 2 1 3 2 1 1 1 2 3 2 2 1 2 2 2 2 2 [6180] 2 2 2 1 3 3 2 2 3 2 2 2 2 1 2 1 1 3 1 1 2 3 3 2 2 1 2 2 2 2 3 4 3 1 3 3 2 [6217] 3 2 1 1 4 1 2 1 1 1 2 2 3 4 2 1 4 1 2 4 4 1 3 2 2 1 2 1 3 3 1 1 1 1 2 1 2 [6254] 3 2 1 1 2 3 2 2 2 2 2 1 2 1 2 3 1 2 2 3 3 3 3 4 3 1 3 3 2 3 4 2 2 3 2 2 3 [6291] 2 1 1 4 2 2 2 3 1 2 2 2 3 3 1 1 2 1 2 4 2 2 2 2 1 1 2 3 2 2 2 1 1 1 1 2 2 [6328] 2 2 1 1 2 2 2 2 2 2 3 3 2 2 2 3 2 1 2 1 1 2 2 2 3 2 2 2 3 1 2 2 3 2 1 1 2 [6365] 3 3 2 2 2 2 3 2 1 2 1 4 1 3 2 3 1 2 2 2 3 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 [6402] 2 2 1 1 2 3 2 1 3 3 1 3 3 2 2 1 1 1 2 2 4 4 2 2 4 3 2 3 2 1 3 1 2 1 1 3 4 [6439] 2 4 1 1 2 3 2 2 1 1 2 2 1 1 1 4 3 2 4 1 2 2 3 2 3 2 1 2 1 1 1 2 3 1 1 1 1 [6476] 2 2 1 1 2 2 2 1 2 1 2 3 2 1 2 3 3 1 3 3 1 1 2 1 3 3 1 1 2 4 2 2 1 3 2 2 2 [6513] 3 2 4 3 2 2 1 1 2 1 2 2 2 2 2 2 3 2 3 3 1 3 1 1 1 2 4 2 2 2 2 2 2 2 2 2 3 [6550] 3 2 2 3 3 1 1 4 2 1 3 2 1 2 2 3 2 2 1 3 2 2 2 2 1 2 2 2 4 2 1 3 4 1 1 4 2 [6587] 2 2 2 2 3 2 2 2 3 1 1 2 3 2 3 1 2 2 2 3 1 3 3 1 4 2 2 2 1 1 2 3 3 1 2 3 1 [6624] 1 2 3 2 1 2 1 3 3 3 3 2 2 2 2 2 2 2 2 2 2 3 1 4 2 2 1 2 3 2 1 3 2 3 2 3 3 [6661] 2 3 3 1 1 3 2 2 2 3 3 1 3 1 3 1 1 2 2 2 1 2 2 4 2 1 1 1 4 3 1 2 2 2 1 3 2 [6698] 2 1 2 1 1 2 3 1 2 2 2 1 2 3 2 2 3 2 3 3 1 1 1 2 1 3 2 2 2 3 2 1 1 2 2 1 2 [6735] 3 2 3 2 2 1 2 1 2 3 1 1 2 2 4 2 1 3 1 1 1 1 2 2 2 2 1 3 3 1 2 4 3 1 2 2 4 [6772] 3 2 3 3 1 2 1 1 2 3 2 2 2 2 4 4 2 3 1 2 2 1 3 2 2 2 3 2 2 3 3 2 2 2 3 2 2 [6809] 2 3 3 1 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 3 3 2 2 2 2 3 2 2 2 2 2 2 [6846] 2 1 2 3 3 3 1 3 1 4 1 2 1 1 3 3 4 3 3 1 2 2 2 1 1 4 2 1 3 3 4 1 3 3 4 3 1 [6883] 4 2 1 1 1 2 2 1 1 3 2 3 2 3 3 2 3 1 2 2 2 1 2 1 1 1 3 1 1 3 4 2 3 2 2 2 1 [6920] 2 1 2 1 2 1 2 1 2 2 3 3 1 4 4 2 1 1 1 3 3 2 2 2 3 2 1 1 2 2 2 1 1 2 3 1 1 [6957] 2 3 3 3 1 1 1 2 2 3 1 2 3 2 1 1 2 2 2 1 3 1 3 3 2 4 3 1 3 3 3 1 3 1 1 1 2 [6994] 2 2 2 1 3 2 2 4 2 2 2 2 1 1 1 2 2 3 3 1 2 2 1 3 2 2 1 3 1 1 1 3 3 3 2 1 3 [7031] 2 4 1 2 2 3 1 2 2 1 3 2 3 3 3 3 2 2 4 3 1 1 3 2 1 3 1 2 4 4 3 2 2 1 1 2 2 [7068] 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 3 2 2 2 2 2 3 1 3 2 2 2 3 2 1 3 2 2 [7105] 3 1 2 3 2 2 2 1 1 2 3 2 1 1 1 3 3 1 2 2 1 4 1 3 3 2 4 2 4 1 1 3 2 3 1 2 1 [7142] 1 4 2 2 2 1 2 1 1 1 2 3 1 1 3 2 2 1 2 2 3 3 2 2 2 2 2 1 2 1 2 4 3 1 4 3 1 [7179] 1 1 2 1 1 3 4 3 3 3 4 3 2 2 2 4 1 2 2 1 2 3 4 3 2 3 2 2 2 2 2 1 2 2 2 2 3 [7216] 1 3 2 3 2 2 1 2 3 3 2 2 1 1 1 4 2 2 1 2 1 3 3 3 1 3 2 1 2 1 2 2 2 2 3 3 2 [7253] 3 2 2 2 1 1 3 1 3 1 2 2 3 1 2 3 2 3 1 1 3 2 1 3 1 2 3 2 2 2 1 1 1 1 1 2 1 [7290] 2 3 2 1 1 1 2 1 2 1 1 1 3 3 4 3 3 2 3 1 3 2 1 3 2 4 2 1 2 4 3 2 2 1 2 2 2 [7327] 1 1 1 1 3 1 3 1 1 3 1 2 3 1 1 3 1 2 3 3 4 1 2 2 2 3 2 4 2 2 2 2 3 1 2 2 2 [7364] 1 2 2 2 1 2 3 1 2 1 1 3 2 2 3 2 2 2 3 2 2 2 2 3 2 2 3 2 3 2 2 1 3 1 1 2 2 [7401] 2 2 2 3 2 1 2 3 2 2 2 2 2 3 2 2 2 2 2 3 1 1 2 3 2 2 3 2 2 2 2 3 2 2 2 1 1 [7438] 1 3 1 3 2 2 1 2 2 1 4 2 3 1 2 3 3 2 3 1 3 1 1 3 3 1 2 3 2 1 2 2 2 3 1 1 4 [7475] 2 2 3 3 3 2 3 2 2 2 2 1 3 2 2 3 1 1 1 2 2 2 3 2 1 2 2 1 1 2 1 1 3 1 2 2 4 [7512] 4 1 3 1 1 3 3 3 1 1 2 1 3 3 2 2 2 2 2 4 4 1 2 1 1 1 2 1 2 3 2 4 1 1 2 3 3 [7549] 3 2 1 3 3 3 2 3 4 3 3 4 2 3 3 1 2 2 2 2 2 1 2 1 1 2 3 2 1 4 2 3 2 1 1 4 1 [7586] 3 1 2 1 1 2 1 2 1 3 3 2 1 1 3 1 1 1 2 3 2 1 2 3 1 1 3 2 3 3 1 1 2 1 1 1 3 [7623] 1 1 1 3 2 4 1 1 2 3 2 4 2 2 3 3 1 3 2 1 2 2 3 1 1 3 3 1 1 2 2 2 4 1 3 3 3 [7660] 1 2 1 1 2 1 2 2 2 2 2 2 1 2 2 1 1 1 3 2 1 1 1 1 3 1 1 3 2 2 3 1 2 3 1 1 3 [7697] 2 2 2 2 2 1 1 3 2 1 2 3 2 3 2 1 3 3 3 1 3 1 1 3 2 2 2 3 2 2 3 1 3 1 2 1 1 [7734] 2 1 2 2 1 1 2 3 2 1 2 1 1 3 1 1 2 2 3 3 1 3 3 1 2 2 3 2 2 2 1 1 3 1 1 1 2 [7771] 3 1 2 2 2 1 1 2 2 3 2 3 2 2 2 2 1 1 2 2 3 2 2 2 3 2 2 2 2 2 2 3 2 2 1 3 2 [7808] 3 2 2 3 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 3 1 1 3 1 2 2 2 3 3 1 3 2 1 1 2 2 1 [7845] 2 2 1 3 2 2 3 1 2 2 1 2 2 1 1 2 2 3 3 3 3 1 3 2 2 3 3 3 4 3 2 1 2 2 1 1 4 [7882] 1 1 2 3 1 1 2 1 2 2 3 4 2 3 2 3 1 2 3 2 1 1 1 1 1 1 2 2 1 1 2 3 3 3 1 3 2 [7919] 2 1 3 2 1 2 4 1 2 2 3 1 1 2 3 1 1 2 1 1 1 3 1 2 3 2 4 2 1 3 2 2 2 2 1 1 1 [7956] 2 2 3 3 3 1 4 3 3 4 2 1 1 1 1 1 3 3 1 1 1 3 2 3 2 2 2 2 3 1 2 1 2 4 2 2 2 [7993] 3 2 2 1 3 3 3 2 2 1 1 1 3 1 3 2 3 3 4 1 2 2 2 2 3 1 2 2 2 2 2 2 2 3 3 3 1 [8030] 3 1 1 3 3 1 2 1 1 2 2 2 1 3 2 2 1 3 1 1 4 4 4 3 2 3 2 2 2 2 3 3 1 1 2 2 4 [8067] 3 3 1 2 2 2 1 2 1 2 2 2 2 2 2 3 1 1 2 2 1 2 1 3 2 2 2 3 2 4 3 3 2 2 2 1 1 [8104] 3 4 1 1 2 2 1 1 1 2 3 1 4 4 2 2 2 2 3 2 3 2 4 1 1 3 1 2 2 2 2 1 3 3 2 3 2 [8141] 4 3 2 2 2 1 2 3 2 1 2 1 3 1 1 2 2 3 2 2 3 1 3 2 2 2 3 1 1 3 2 2 2 3 1 2 1 [8178] 3 1 2 2 3 1 2 1 3 1 2 1 1 3 2 2 1 2 1 3 4 3 1 1 2 3 1 1 2 3 1 4 4 4 1 1 1 [8215] 1 1 2 1 4 4 3 2 1 1 3 2 1 4 1 2 2 2 3 2 3 1 2 2 1 2 2 2 2 3 2 1 2 2 2 1 2 [8252] 2 3 4 1 1 2 1 2 2 1 2 1 3 2 1 1 3 2 2 3 1 3 4 3 2 1 3 2 4 2 1 1 1 1 2 3 1 [8289] 1 1 3 2 1 1 3 2 1 1 1 2 2 2 1 3 3 1 1 2 2 1 2 3 1 2 2 2 2 1 1 1 2 1 2 2 1 [8326] 2 1 3 2 3 2 1 1 3 2 3 2 1 1 2 3 1 2 2 2 1 2 2 1 1 3 2 1 1 2 3 2 2 2 2 2 3 [8363] 2 1 3 2 2 3 3 2 4 1 3 1 3 2 3 3 2 2 2 3 3 3 4 3 2 1 1 1 1 4 1 2 1 4 4 1 2 [8400] 1 1 2 3 1 3 3 2 3 3 1 1 3 2 3 2 1 1 2 2 3 2 1 1 2 3 3 1 1 3 3 3 2 2 3 3 1 [8437] 2 2 3 2 1 3 1 3 1 1 3 2 2 2 2 2 3 2 3 1 1 2 2 1 2 2 3 1 2 1 2 1 1 2 3 3 3 [8474] 3 3 2 2 2 2 1 2 3 2 2 2 2 2 3 2 2 2 2 2 2 1 3 3 3 1 2 3 3 1 2 2 1 3 2 4 2 [8511] 1 1 3 3 3 2 2 1 3 2 1 3 1 1 2 2 3 3 2 2 2 2 2 3 2 2 1 2 3 1 1 1 1 2 1 3 2 [8548] 2 3 1 1 2 3 2 3 2 4 1 3 2 1 3 3 2 2 1 1 2 2 2 2 3 3 1 3 3 3 2 1 1 3 3 1 2 [8585] 2 3 3 3 1 3 2 2 1 4 3 1 2 1 1 3 3 2 2 3 3 1 3 3 2 3 1 1 1 3 3 2 2 3 1 1 2 [8622] 2 3 2 2 3 3 1 2 3 3 3 1 1 1 1 2 3 3 2 2 1 3 2 1 1 3 3 1 3 1 2 1 2 2 3 2 1 [8659] 2 2 1 3 1 3 2 2 2 1 2 2 2 2 2 2 3 1 1 3 2 3 2 2 2 3 2 1 2 2 3 2 3 2 1 2 2 [8696] 2 2 3 2 2 2 2 2 2 2 2 2 1 1 1 2 3 2 3 2 4 1 3 1 1 3 3 2 2 2 1 3 1 3 3 1 1 [8733] 3 2 2 3 2 3 2 3 4 1 1 2 2 2 2 3 1 1 3 1 3 2 3 1 3 3 4 2 4 1 3 1 1 2 3 1 1 [8770] 3 4 2 2 2 3 1 2 1 1 4 2 2 2 2 3 1 2 1 3 3 4 2 1 4 1 3 3 2 3 1 1 2 3 1 4 1 [8807] 2 4 2 1 1 2 1 1 2 2 1 3 3 3 1 3 2 1 1 2 2 1 3 1 2 2 2 1 3 2 1 2 3 1 2 2 2 [8844] 2 2 3 3 3 1 2 1 1 2 2 1 2 3 2 3 1 2 1 3 3 3 2 2 2 2 4 2 1 2 2 1 3 1 3 1 2 [8881] 2 2 2 1 2 1 2 1 2 2 1 2 1 1 3 2 2 3 2 1 2 3 3 1 1 1 3 3 4 3 2 1 3 3 1 2 2 [8918] 3 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 3 2 2 2 2 2 2 2 1 2 2 1 1 2 4 2 3 1 [8955] 1 3 2 2 1 3 3 2 2 2 3 1 4 1 4 3 3 3 3 2 3 3 4 1 1 3 3 3 3 3 4 2 3 2 1 2 1 [8992] 2 1 2 2 3 1 1 3 2 2 3 2 1 2 2 2 3 1 1 4 4 3 1 2 3 3 3 3 1 2 1 3 4 3 1 3 2 [9029] 1 1 1 2 4 3 2 2 2 1 2 4 2 2 2 2 3 1 2 3 3 3 2 4 3 1 2 1 2 3 2 2 2 2 2 1 2 [9066] 1 2 4 2 2 2 4 3 3 2 1 2 3 2 3 2 2 2 1 2 3 1 3 2 1 1 1 1 1 3 3 2 1 1 2 2 1 [9103] 2 3 3 3 3 1 2 1 4 2 2 3 3 1 1 3 3 3 1 1 3 2 3 1 1 2 2 1 1 1 3 2 2 2 1 2 1 [9140] 1 3 4 3 2 3 2 2 3 2 2 2 2 2 1 2 2 2 2 3 2 2 2 3 2 2 2 2 2 3 2 2 2 2 2 2 2 [9177] 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 3 3 4 3 2 3 4 3 2 2 3 2 2 3 1 2 2 2 3 1 [9214] 1 2 2 1 2 1 3 1 3 3 1 3 1 1 1 3 3 2 4 3 2 3 1 1 2 3 1 1 1 3 1 1 2 2 3 2 1 [9251] 1 1 1 3 1 3 2 3 4 4 3 2 1 2 1 1 2 3 1 1 3 2 2 2 1 1 3 2 2 2 3 1 3 3 2 2 3 [9288] 2 3 1 1 2 1 1 2 2 4 1 2 1 3 4 2 2 2 3 2 2 1 1 4 1 1 4 3 2 1 2 3 3 2 3 1 1 [9325] 1 2 2 1 4 1 3 3 3 2 2 3 2 3 2 2 3 2 1 3 1 2 2 3 2 1 2 3 3 2 1 1 3 3 1 3 1 [9362] 1 3 1 2 3 1 2 3 2 1 2 3 1 2 3 1 3 2 2 2 1 3 1 1 2 2 2 3 3 2 3 3 3 2 2 2 3 [9399] 3 1 1 1 1 2 3 3 1 3 1 4 3 1 3 3 2 2 2 1 1 1 2 2 1 2 3 3 2 1 3 3 1 1 2 2 2 [9436] 3 1 1 4 2 2 3 1 2 2 2 4 1 1 3 3 1 3 2 1 3 2 2 3 2 3 1 1 3 2 1 2 3 3 1 1 3 [9473] 2 3 1 1 3 2 2 3 3 3 1 3 2 3 3 3 2 1 2 2 1 1 1 2 2 1 1 2 3 2 2 2 2 2 2 3 1 [9510] 2 3 3 2 1 2 3 2 2 2 2 3 2 2 2 3 3 1 2 2 1 1 2 3 2 3 4 2 2 2 1 2 2 3 3 4 1 [9547] 1 2 2 2 2 2 3 3 1 3 2 1 3 2 1 1 4 1 1 2 1 3 2 1 1 2 1 1 4 2 1 3 2 3 1 1 2 [9584] 1 2 2 1 4 3 4 1 1 3 3 3 1 2 2 1 2 2 1 2 3 1 2 3 1 3 3 3 1 1 2 1 2 1 3 3 3 [9621] 1 2 2 1 4 2 2 3 3 1 1 3 3 2 1 1 2 1 2 2 3 3 2 1 2 3 3 4 2 3 2 2 2 3 1 1 3 [9658] 4 3 2 2 1 1 1 2 2 2 1 2 1 3 3 4 1 3 1 1 3 1 1 4 3 3 1 3 2 2 1 1 2 2 3 2 2 [9695] 2 2 2 2 1 1 3 1 3 3 3 2 2 2 3 2 1 3 1 2 1 3 1 3 1 2 3 1 2 2 3 3 3 1 3 2 1 [9732] 1 2 2 1 3 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 3 1 2 1 3 2 [9769] 3 3 2 2 4 1 3 1 2 1 2 2 3 2 2 1 2 1 1 1 1 2 3 4 1 3 1 2 1 2 2 1 1 1 4 2 2 [9806] 3 2 1 2 2 2 2 1 3 1 2 1 2 2 2 2 3 1 2 2 3 2 1 3 2 3 2 4 2 3 1 2 2 3 1 3 1 [9843] 3 1 2 1 2 3 4 3 2 2 3 1 1 3 3 2 2 2 2 3 1 3 2 2 2 2 3 2 2 2 Levels: 1 2 3 4 5
4.3.3 - Normalization Min-Max Scaling :¶
We used a technique called max-min normalization to make sure our data was consistently scaled. This method adjusts the values of certain attributes to fit within a range from 0 to 1. We applied this normalization to three specific attributes: age, Arrival Delay in Minutes, and Departure Delay in Minutes. Normalizing the dataset in this way makes the attributes more uniform and comparable, which helps us perform accurate analysis and modeling for predicting satisfaction, as demonstrated in the results.
normalize <- function(x) { return ((x - min(x))/ (max(x)- min(x))) }
data$Age=normalize(data$Age)
data$Arrival.Delay.in.Minutes=normalize(data$Arrival.Delay.in.Minutes)
data$Departure.Delay.in.Minutes=normalize(data$Departure.Delay.in.Minutes)
head(data)
| satisfaction | Gender | Customer.Type | Age | Type.of.Travel | Class | Flight.Distance | Seat.comfort | Departure.Arrival.time.convenient | Food.and.drink | ⋯ | Online.support | Ease.of.Online.booking | On.board.service | Leg.room.service | Baggage.handling | Checkin.service | Cleanliness | Online.boarding | Departure.Delay.in.Minutes | Arrival.Delay.in.Minutes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <dbl> | <int> | <int> | <dbl> | <int> | <int> | <fct> | <int> | <int> | <int> | ⋯ | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <dbl> | <dbl> | |
| 23 | 1 | 1 | 1 | 0.24657534 | 1 | 1 | 2 | 0 | 1 | 0 | ⋯ | 4 | 2 | 4 | 1 | 3 | 1 | 3 | 2 | 0 | 0 |
| 30 | 1 | 1 | 1 | 0.38356164 | 1 | 1 | 3 | 0 | 1 | 0 | ⋯ | 4 | 2 | 2 | 3 | 4 | 4 | 3 | 4 | 0 | 0 |
| 34 | 1 | 1 | 1 | 0.26027397 | 1 | 1 | 2 | 0 | 1 | 0 | ⋯ | 4 | 4 | 1 | 4 | 4 | 2 | 3 | 4 | 0 | 0 |
| 37 | 1 | 1 | 1 | 0.04109589 | 1 | 1 | 3 | 0 | 1 | 0 | ⋯ | 4 | 4 | 4 | 3 | 3 | 1 | 4 | 4 | 0 | 0 |
| 50 | 1 | 2 | 1 | 0.36986301 | 1 | 1 | 2 | 0 | 1 | 0 | ⋯ | 4 | 4 | 1 | 1 | 2 | 3 | 2 | 4 | 0 | 0 |
| 64 | 1 | 1 | 1 | 0.05479452 | 1 | 1 | 2 | 0 | 1 | 0 | ⋯ | 3 | 3 | 1 | 2 | 3 | 2 | 3 | 3 | 0 | 0 |
4.4- Feature selection :¶
To simplify our predictive model, we'll use a feature selection technique known as Recursive Feature Elimination (RFE). This method is commonly used to pick out the most important input variables for predicting our target variable, which in our case is "satisfaction." Additionally, we'll utilize the varImp function, which helps us assess the importance of different variables in our analysis.
install.packages("mlbench")
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
install.packages("caret")
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
install.packages("randomForest")
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
library(randomForest)
randomForest 4.6-14
Type rfNews() to see new features/changes/bug fixes.
Attaching package: ‘randomForest’
The following object is masked from ‘package:outliers’:
outlier
The following object is masked from ‘package:ggplot2’:
margin
The following object is masked from ‘package:dplyr’:
combine
# ensure the results are repeatable
set.seed(7)
# load the library
library(mlbench)
library(caret)
# Convert the class label to a factor
data$satisfaction <- as.factor(data$satisfaction)
# Separate the predictors and the class label
predictors <- data[, -23] # Excluding the class label (satisfaction)
class_label <- data$satisfaction
# Train a Random Forest model
model <- randomForest(predictors, class_label, importance = TRUE)
# Get the variable importance
importance <- importance(model)
ranked_features <- sort(importance[, "MeanDecreaseGini"], decreasing = TRUE)
# Print the ranked features
print(ranked_features)
barplot(ranked_features, horiz = TRUE, col = c("lightblue2"), las = 1, main = "Airline satisfaction Variable Importance Ranking")
Loading required package: lattice
satisfaction Inflight.entertainment
2936.58499 555.70793
Seat.comfort Ease.of.Online.booking
232.46321 217.84534
Online.support Customer.Type
165.16426 100.34501
On.board.service Online.boarding
92.20038 76.36363
Class Leg.room.service
76.07383 71.11721
Food.and.drink Gender
64.07204 48.35584
Cleanliness Baggage.handling
35.24962 33.75700
Type.of.Travel Checkin.service
32.25740 29.85412
Age Departure.Arrival.time.convenient
24.83237 22.60883
Inflight.wifi.service Gate.location
21.27955 17.93849
Departure.Delay.in.Minutes Flight.Distance
15.15172 11.66504
After we do the feature selection we notice that the least importante attribute are (Flight.Distance, Departure.Delay.in.Minutes, Gate location) Therefore, we will remove them
#delete the coloumns
data <-data[,!names(data) %in% c("Flight.Distance", "Gate.location", "Departure.Delay.in.Minutes")]
Finally an overall head for our dataset
head(data)
| satisfaction | Gender | Customer.Type | Age | Type.of.Travel | Class | Seat.comfort | Departure.Arrival.time.convenient | Food.and.drink | Inflight.wifi.service | Inflight.entertainment | Online.support | Ease.of.Online.booking | On.board.service | Leg.room.service | Baggage.handling | Checkin.service | Cleanliness | Online.boarding | Arrival.Delay.in.Minutes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <fct> | <int> | <int> | <dbl> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <dbl> | |
| 23 | 1 | 1 | 1 | 0.24657534 | 1 | 1 | 0 | 1 | 0 | 2 | 0 | 4 | 2 | 4 | 1 | 3 | 1 | 3 | 2 | 0 |
| 30 | 1 | 1 | 1 | 0.38356164 | 1 | 1 | 0 | 1 | 0 | 0 | 4 | 4 | 2 | 2 | 3 | 4 | 4 | 3 | 4 | 0 |
| 34 | 1 | 1 | 1 | 0.26027397 | 1 | 1 | 0 | 1 | 0 | 4 | 0 | 4 | 4 | 1 | 4 | 4 | 2 | 3 | 4 | 0 |
| 37 | 1 | 1 | 1 | 0.04109589 | 1 | 1 | 0 | 1 | 0 | 4 | 0 | 4 | 4 | 4 | 3 | 3 | 1 | 4 | 4 | 0 |
| 50 | 1 | 2 | 1 | 0.36986301 | 1 | 1 | 0 | 1 | 0 | 4 | 0 | 4 | 4 | 1 | 1 | 2 | 3 | 2 | 4 | 0 |
| 64 | 1 | 1 | 1 | 0.05479452 | 1 | 1 | 0 | 1 | 0 | 3 | 0 | 3 | 3 | 1 | 2 | 3 | 2 | 3 | 3 | 0 |
4.4.1 imbalanced dataset problem:¶
Addressing imbalanced data is pivotal for constructing machine learning models that are both effective and fair. A strategic amalgamation of resampling techniques, judicious algorithm selection, and meticulous evaluation metrics can notably enhance model performance on imbalanced datasets. In this context, we chosen method involves oversampling to balance our dataset , which entails augmenting the number of instances in the class to rectify the class imbalance. This approach aims to bolster the model's ability to adequately capture and learn from the underrepresented class, thereby fostering a more balanced and robust predictive outcome.
install.packages("ROSE")
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
# upscaling the data
data<-upSample(data[,-1],data$satisfaction, yname="satisfaction")
plot(data$satisfaction)
# checking the number of stroke/ non-stroke observations
prop.table(table(data$satisfaction))
title(main="Data after oversampling", xlab="satisfaction", ylab="observations")
0 1 0.5 0.5
#Number of rows
nrow(data)
delete random rows
# Set a random seed for reproducibility
set.seed(1234)
# Determine the number of rows you want to delete
num_rows_to_delete <- 5000 # Adjust this number as needed
# Generate random row indices to delete
rows_to_delete <- sample(nrow(data), num_rows_to_delete)
# Keep only the rows that are not in the rows_to_delete vector
data <- data[-rows_to_delete, ]
row_count <- nrow(data)
print(row_count)
[1] 5894
install.packages("openxlsx") # Install the openxlsx package if you haven't already
library(openxlsx)
write.xlsx(data, file = "ProjectG4.xlsx", row.names = FALSE)
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified) Warning message: “Please use 'rowNames' instead of 'row.names'”
head(data)
| Gender | Customer.Type | Age | Type.of.Travel | Class | Seat.comfort | Departure.Arrival.time.convenient | Food.and.drink | Inflight.wifi.service | Inflight.entertainment | Online.support | Ease.of.Online.booking | On.board.service | Leg.room.service | Baggage.handling | Checkin.service | Cleanliness | Online.boarding | Arrival.Delay.in.Minutes | satisfaction | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <int> | <int> | <dbl> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <dbl> | <fct> | |
| 1 | 2 | 1 | 0.71232877 | 1 | 2 | 1 | 0 | 1 | 1 | 4 | 3 | 2 | 5 | 3 | 3 | 3 | 2 | 3 | 0.483974359 | 0 |
| 2 | 2 | 1 | 0.24657534 | 1 | 1 | 1 | 0 | 1 | 4 | 1 | 4 | 4 | 1 | 2 | 3 | 5 | 1 | 4 | 0.198717949 | 0 |
| 3 | 2 | 1 | 0.67123288 | 1 | 3 | 1 | 0 | 1 | 4 | 4 | 5 | 3 | 3 | 1 | 3 | 3 | 3 | 4 | 0.003205128 | 0 |
| 4 | 2 | 1 | 0.32876712 | 1 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 2 | 4 | 1 | 4 | 5 | 5 | 2 | 0.000000000 | 0 |
| 6 | 2 | 1 | 0.67123288 | 1 | 1 | 1 | 0 | 1 | 2 | 1 | 4 | 2 | 5 | 4 | 5 | 5 | 4 | 2 | 0.000000000 | 0 |
| 7 | 2 | 1 | 0.09589041 | 1 | 1 | 1 | 0 | 1 | 4 | 1 | 4 | 4 | 1 | 3 | 3 | 1 | 1 | 4 | 0.000000000 | 0 |
5 - Data Mining Technique :¶
Data mining plays a pivotal role in forecasting satisfaction probabilities through the application of classification and clustering techniques. When data mining algorithms are applied to a vast dataset encompassing diverse features, they unveil valuable patterns and relationships. The approach involves employing a set of data mining techniques, encompassing both classification and clustering methodologies. For classification tasks, the plan integrates essential metrics like the Gini index, gain ratio, and information gain, pivotal in decision tree-based algorithms. This process will utilize methods from R packages such as 'ipred', 'rpart', 'caret', and 'multcomp', enabling the construction of predictive models adept at categorizing individuals into distinct satisfaction groups based on their attributes. Additionally, for clustering analysis, the k-means algorithm will be employed to identify cohesive groups within the dataset, leveraging the 'factoextra' package in R to aid in interpreting and visualizing the resulting clusters. This comprehensive approach aims to effectively classify individuals and uncover distinct clusters, facilitating a deeper understanding of satisfaction dynamics within the dataset
6-Evaluation and Comparison:¶
6.1 - Classification :¶
In the classification phase, post-balancing our dataset, we employ a classification algorithm to categorize each data point into predefined classes based on its attributes. The pertinent features, having undergone cleaning and formatting in the preprocessing stage, are selected for this purpose. The chosen model is subsequently trained on the prepared data, enabling it to predict the category of new, unseen data accurately. using information gain , Gini index,Gain ratio This pivotal step lays the foundation for making informed decisions or predictions based on our dataset.
6.1.1 - information gain :¶
Information Gain plays a vital role in decision tree algorithms by identifying the most informative features for dataset splitting. This process involves evaluating multiple potential splits, enabling decision trees to effectively partition data. As a result, decision trees become powerful tools for classification tasks.
First split :¶
To ensure reproducibility, we set a seed, then split our dataset into training (70%) and test (30%) sets using the holdout method. We use the ctree function from the party package, which employs information gain (ID) as a criterion for splitting.
install.packages('ipred')
library(ipred)
install.packages('rpart')
install.packages('rpart.plot')
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified) Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified) Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
library(rpart)
library(rpart.plot)
install.packages("caTools")
library(caTools)
set.seed(123)
#split the dataset into training and testing
split = sample.split(data$satisfaction, SplitRatio = 0.70)
training_set = subset(data, split == TRUE)
test_set = subset(data, split == FALSE)
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
tree <- rpart(satisfaction ~ ., data = training_set,method = 'class', parms= list(split="information"))
rpart.plot(tree)
In the initial tree model, we split the dataset into training and test sets sized at 70% and 30%, respectively. Illustrated in the figure, the root node, denoted as 'inflight.entertainment,' serves as the starting point for classification due to its higher information gain. The dataset's class distribution comprises approximately 47% for class 1 ('seat.comfort') and 53% for class 2 ('Ease.of.online.booking').
The tree progresses by branching based on the values of 'inflight.entertainment.' If the value equals 1, the majority of instances fall into 'seat.comfort' classes: 0 (42%) and 1 (6%). If 'seat.comfort' is greater than or equal to 1, it leads to classifications of 0 (40%) and 1 (2%). In such cases, the tree terminates.
Otherwise, instances are further classified based on 'Ease.of.online.booking.' When 'inflight.entertainment' equals 1, instances are classified into 'seat.comfort' 1 (38%) and 1 'seat.comfort' with a probability of 15%. Alternatively, if 'inflight.entertainment' equals 0, instances are classified into 'seat.comfort' 0 (10%) and 1 (2%)."
prediction <- predict(tree, newdata = test_set,type = 'class')
cm <- table(test_set$satisfaction, prediction)
cm
accuracy <- sum(diag(cm)) / sum(cm)
print(paste('Accuracy on test data is ', accuracy))
prediction
0 1
0 744 134
1 127 763
[1] "Accuracy on test data is 0.85237556561086"
library(caret)
# Calculate confusion matrix
cm <- confusionMatrix(prediction, test_set$satisfaction)
# Extract metrics using the confusion matrix
sensitivity <- cm$byClass['Sensitivity']
specificity <- cm$byClass['Specificity']
precision <- cm$byClass['Pos Pred Value']
# Print results
print(paste('Sensitivity (True Positive Rate): ', sensitivity))
print(paste('Specificity (True Negative Rate): ', specificity))
print(paste('Precision (Positive Predictive Value): ', precision))
[1] "Sensitivity (True Positive Rate): 0.847380410022779" [1] "Specificity (True Negative Rate): 0.857303370786517" [1] "Precision (Positive Predictive Value): 0.854190585533869"
Second split:¶
To ensure reproducibility, we set a seed, then split our dataset into training (60%) and test (40%) sets using the holdout method. We use the ctree function from the party package, which employs information gain (ID) as a criterion for splitting.
#split the dataset into training and testing
split = sample.split(data$satisfaction, SplitRatio = 0.60)
training_set = subset(data, split == TRUE)
test_set = subset(data, split == FALSE)
tree <- rpart(satisfaction ~ ., data = training_set,method = 'class', parms= list(split="information"))
rpart.plot(tree)
In the second tree model, we partitioned the dataset into training and test sets with sizes of 60% and 40%, respectively. Illustrated in the figure, the root node, 'inflight.entertainment,' plays a crucial role as the starting point for the classification process due to its highest information gain. The dataset exhibits a distribution of approximately 48% for class 1 ('seat.comfort') and 52% for class 2 ('Ease.of.online.booking'). The tree continues to branch based on the values of 'inflight.entertainment.' If the value equals 1, the majority of instances fall into 'Ease.of.online.booking,' constituting 15% for 'seat.comfort' and 38%. If 'seat.comfort' is less than 5, it leads to classifications of 1 (2%). If 'seat.comfort' equals 0, instances are classified into 'checkin.service,' resulting in 0 (13%) and further branching into 0 (3%) and 1 (10%). In such cases, the tree terminates. Otherwise, instances are further classified based on 'seat.comfort.' When 'inflight.entertainment' equals 1, instances are classified into 'seat.comfort' 1 (5%) and 1 'seat.comfort' with a probability of 42%. If 'seat.comfort' is greater than or equal to 1, it leads to classifications of 0 (40%) and 1 (2%). In this case, the tree terminates.
prediction <- predict(tree, newdata = test_set,type = 'class')
cm <- table(test_set$satisfaction, prediction)
cm
accuracy <- sum(diag(cm)) / sum(cm)
print(paste('Accuracy on test data is ', accuracy))
prediction
0 1
0 911 259
1 87 1100
[1] "Accuracy on test data is 0.853203224437845"
library(caret)
# Calculate confusion matrix
cm <- confusionMatrix(prediction, test_set$satisfaction)
# Extract metrics using the confusion matrix
sensitivity <- cm$byClass['Sensitivity']
specificity <- cm$byClass['Specificity']
precision <- cm$byClass['Pos Pred Value']
# Print results
print(paste('Sensitivity (True Positive Rate): ', sensitivity))
print(paste('Specificity (True Negative Rate): ', specificity))
print(paste('Precision (Positive Predictive Value): ', precision))
[1] "Sensitivity (True Positive Rate): 0.778632478632479" [1] "Specificity (True Negative Rate): 0.92670598146588" [1] "Precision (Positive Predictive Value): 0.912825651302605"
Third split :¶
To ensure reproducibility, we set a seed, then split our dataset into training (85%) and test (15%) sets using the holdout method. We use the ctree function from the party package, which employs information gain (ID) as a criterion for splitting.
#split the dataset into training and testing
split = sample.split(data$satisfaction, SplitRatio = 0.85)
training_set = subset(data, split == TRUE)
test_set = subset(data, split == FALSE)
tree <- rpart(satisfaction ~ ., data = training_set,method = 'class', parms= list(split="information"))
rpart.plot(tree)
In the third tree model, we divided the dataset into training and test sets with sizes of 85% and 15%, respectively. As illustrated in the figure, the root node, represented by 'inflight.entertainment,' initiates the classification process due to its highest gain. The dataset displays a distribution of approximately 47% for class 1 ('seat.comfort') and 53% for class 2 ('Ease.of.online.booking'). The tree progresses by branching based on the values of 'inflight.entertainment.' If it equals 1, the majority of instances are categorized into 'Ease.of.online.booking,' comprising 14% for 'seat.comfort' and 38%. For 'seat.comfort' less than 5, the classification results in 1 (2%). Yet, if 'inflight.entertainment' equals 0, instances are classified into 'checkin.service,' resulting in 0 (3%) and 1 (9%). In such cases, the tree terminates. Alternatively, if instances aren't classified based on the preceding conditions, they are further evaluated based on 'seat.comfort.' If it equals 1, instances are classified into 1 (5%) and 0 (42%). For 'seat.comfort' greater than or equal to 1, the classification results in 0 (40%) and 1 (2%).
prediction <- predict(tree, newdata = test_set,type = 'class')
cm <- table(test_set$satisfaction, prediction)
cm
accuracy <- sum(diag(cm)) / sum(cm)
print(paste('Accuracy on test data is ', accuracy))
prediction
0 1
0 339 100
1 31 414
[1] "Accuracy on test data is 0.851809954751131"
library(caret)
# Calculate confusion matrix
cm <- confusionMatrix(prediction, test_set$satisfaction)
# Extract metrics using the confusion matrix
sensitivity <- cm$byClass['Sensitivity']
specificity <- cm$byClass['Specificity']
precision <- cm$byClass['Pos Pred Value']
# Print results
print(paste('Sensitivity (True Positive Rate): ', sensitivity))
print(paste('Specificity (True Negative Rate): ', specificity))
print(paste('Precision (Positive Predictive Value): ', precision))
[1] "Sensitivity (True Positive Rate): 0.772209567198178" [1] "Specificity (True Negative Rate): 0.930337078651685" [1] "Precision (Positive Predictive Value): 0.916216216216216"
6.1.2 - Analysis :¶
| training (70%) and test (30%) | training (60%) and test (40%) | training (85%) and test (15%) | |
|---|---|---|---|
| Accuracy | 0.852 | 0.853 | 0.851 |
|Precision|0.854|0.912|0.916 |sensitivity|0.847|0.778|0.772| |specificity|0.857|0.926|0.930|
Upon analyzing the provided metrics for different training and test splits, several key observations emerge. Firstly, the accuracies across all three scenarios are closely aligned, signifying a consistent overall performance of the model. Secondly, the split with 60% training and 40% test stands out with the highest precision (0.912). This underscores the model's proficiency in correctly identifying positive cases, showcasing a high ratio of accurate positive predictions relative to the total predicted positives.
Additionally, the split with 85% training and 15% test boasts the highest sensitivity (0.772). This outcome highlights the model's enhanced capability to capture a larger proportion of relevant positive instances. Moreover, in the same split, the model exhibits the highest specificity (0.930), underscoring its superior accuracy in correctly identifying negative cases.
In summary, while the accuracies remain consistent, the 60-40 split excels in precision, emphasizing its strength in positive predictions. On the other hand, the 85-15 split outperforms in sensitivity and specificity, showcasing a heightened ability to capture positive instances and accurately identify negative cases.
6.1.3- Gini index¶
The Gini Index is another criterion used in decision tree algorithms for evaluating the impurity of a dataset. It is a measure of how often a randomly chosen element would be incorrectly classified in a dataset.
First split :¶
split our dataset into training (70%) and test (30%)
set.seed(123)
#split the dataset into training and testing
split = sample.split(data$satisfaction, SplitRatio = 0.70)
training_set = subset(data, split == TRUE)
test_set = subset(data, split == FALSE)
tree <- rpart(satisfaction ~ ., data = training_set,method = 'class')
rpart.plot(tree)
In the first tree, we partitioned the dataset into training and test sets with sizes of 70% and 30%, respectively. As depicted in the figure, the root node, 'inflight.entertainment,' is the starting point for the classification process using the Gini index. The dataset comprises roughly 47% for class 1 ('seat.comfort') and 53% for class 2 ('Ease.of.online.booking'). The tree progresses by branching based on the values of 'inflight.entertainment.' If it equals 1, the majority of instances fall into 'Ease.of.online.booking,' consisting of 15% for 'inflight.entertainment' and 38%. For 'inflight.entertainment' less than 5, the classification results in 1 (4%). However, if 'inflight.entertainment' equals 0, instances are classified into 'seat.comfort,' leading to 1 (1%), and if 'inflight.entertainment' equals 0, instances are classified into 'checkin.service,' resulting in 0 (9%) and 1 (2%). In such cases, the tree terminates. Otherwise, instances are further evaluated based on 'seat.comfort.' If it equals 1, classifications result in 1 (6%) and 1 (42%). For 'seat.comfort' greater than or equal to 1, the classification leads to 0 (40%) and 1 (2%).
prediction <- predict(tree, newdata = test_set,type = 'class')
cm <- table(test_set$satisfaction, prediction)
cm
accuracy <- sum(diag(cm)) / sum(cm)
print(paste('Accuracy on test data is ', accuracy))
prediction
0 1
0 736 142
1 110 780
[1] "Accuracy on test data is 0.857466063348416"
library(caret)
# Calculate confusion matrix
cm <- confusionMatrix(prediction, test_set$satisfaction)
# Extract metrics using the confusion matrix
sensitivity <- cm$byClass['Sensitivity']
specificity <- cm$byClass['Specificity']
precision <- cm$byClass['Pos Pred Value']
# Print results
print(paste('Sensitivity (True Positive Rate): ', sensitivity))
print(paste('Specificity (True Negative Rate): ', specificity))
print(paste('Precision (Positive Predictive Value): ', precision))
[1] "Sensitivity (True Positive Rate): 0.838268792710706" [1] "Specificity (True Negative Rate): 0.876404494382023" [1] "Precision (Positive Predictive Value): 0.869976359338062"
Second split :¶
split our dataset into training (60%) and test (40%)
set.seed(123)
#split the dataset into training and testing
split = sample.split(data$satisfaction, SplitRatio = 0.60)
training_set = subset(data, split == TRUE)
test_set = subset(data, split == FALSE)
tree <- rpart(satisfaction ~ ., data = training_set,method = 'class')
rpart.plot(tree)
In the second tree, we divided the dataset into training and test sets sized at 60% and 40%, respectively. Illustrated in the figure, the root node, 'inflight.entertainment,' initiates the classification process using the Gini index. The dataset showcases an approximate distribution of 47% for class 1 ('seat.comfort') and 53% for class 2 ('Ease.of.online.booking'). The tree progresses by branching based on the values of 'inflight.entertainment.' When it equals 1, the majority of instances are categorized into 'Ease.of.online.booking,' comprising 15% for 'inflight.entertainment' and 38%. If 'inflight.entertainment' is less than 5, the classification results in 1 (4%). However, if 'inflight.entertainment' equals 0, the tree terminates. Otherwise, instances are further assessed based on 'seat.comfort.' If it equals 1, classifications result in 1 (5%) and 0 (42%). For 'seat.comfort' greater than or equal to 1, the classification leads to 0 (40%) and 1 (2%).
prediction <- predict(tree, newdata = test_set,type = 'class')
cm <- table(test_set$satisfaction, prediction)
cm
accuracy <- sum(diag(cm)) / sum(cm)
print(paste('Accuracy on test data is ', accuracy))
prediction
0 1
0 990 180
1 185 1002
[1] "Accuracy on test data is 0.84514212982605"
library(caret)
# Calculate confusion matrix
cm <- confusionMatrix(prediction, test_set$satisfaction)
# Extract metrics using the confusion matrix
sensitivity <- cm$byClass['Sensitivity']
specificity <- cm$byClass['Specificity']
precision <- cm$byClass['Pos Pred Value']
# Print results
print(paste('Sensitivity (True Positive Rate): ', sensitivity))
print(paste('Specificity (True Negative Rate): ', specificity))
print(paste('Precision (Positive Predictive Value): ', precision))
[1] "Sensitivity (True Positive Rate): 0.846153846153846" [1] "Specificity (True Negative Rate): 0.844144903117102" [1] "Precision (Positive Predictive Value): 0.842553191489362"
Third split :¶
split our dataset into training (85%) and test (15%)
set.seed(123)
#split the dataset into training and testing
split = sample.split(data$satisfaction, SplitRatio = 0.85)
training_set = subset(data, split == TRUE)
test_set = subset(data, split == FALSE)
tree <- rpart(satisfaction ~ ., data = training_set,method = 'class')
rpart.plot(tree)
In the third tree, we divided the dataset into training and test sets, sized at 85% and 15%, respectively. Illustrated in the figure, the root node, 'inflight.entertainment,' initiates the classification process using the Gini index. The dataset demonstrates a distribution of approximately 47% for class 1 ('seat.comfort') and 53% for class 2 ('Ease.of.online.booking'). The tree branches further based on the values of 'inflight.entertainment.' If it equals 1, the majority of instances fall into 'Ease.of.online.booking,' consisting of 15% for 'seat.comfort' and 38%. For 'seat.comfort' less than 5, the classification results in 1 (2%). However, if 'inflight.entertainment' equals 0, the tree branches into 'checkin.service' and 'online.boarding,' resulting in 0 (3%) and 1 (10%) for 'checkin.service' and 0 (3%) and 1 (6%) for 'online.boarding.' In such cases, the tree terminates. Otherwise, instances are further assessed based on 'seat.comfort.' If it equals 1, classifications result in 1 (6%) and 0 (42%). For 'seat.comfort' greater than or equal to 1, the classification leads to 0 (40%) and 1 (2%).
prediction <- predict(tree, newdata = test_set,type = 'class')
cm <- table(test_set$satisfaction, prediction)
cm
accuracy <- sum(diag(cm)) / sum(cm)
print(paste('Accuracy on test data is ', accuracy))
prediction
0 1
0 355 84
1 43 402
[1] "Accuracy on test data is 0.856334841628959"
library(caret)
# Calculate confusion matrix
cm <- confusionMatrix(prediction, test_set$satisfaction)
# Extract metrics using the confusion matrix
sensitivity <- cm$byClass['Sensitivity']
specificity <- cm$byClass['Specificity']
precision <- cm$byClass['Pos Pred Value']
# Print results
print(paste('Sensitivity (True Positive Rate): ', sensitivity))
print(paste('Specificity (True Negative Rate): ', specificity))
print(paste('Precision (Positive Predictive Value): ', precision))
[1] "Sensitivity (True Positive Rate): 0.808656036446469" [1] "Specificity (True Negative Rate): 0.903370786516854" [1] "Precision (Positive Predictive Value): 0.891959798994975"
6.1.4 - Analysis :¶
| training (70%) and test (30%) | training (60%) and test (40%) | training (85%) and test (15%) | |
|---|---|---|---|
| Accuracy | 0.857 | 0.845 | 0.856 |
| Precision | 0.869 | 0.842 | 0.891 |
| sensitivity | 0.838 | 0.846 | 0.808 |
| specificity | 0.876 | 0.844 | 0.903 |
Upon analyzing the provided table for different training and test splits:
The 70% training and 30% test split distinguishes itself with the top accuracy and specificity, highlighting a harmonious balance between training and testing. In contrast, the 85% training and 15% test split outshine others in precision, indicating an elevated accuracy in positive predictions.
While there are fluctuations in precision, sensitivity, and specificity across various splits, no discernible pattern emerges to suggest a consistently superior split. The accuracies maintain close proximity in all three scenarios, underscoring a consistent and reliable overall model performance.
6.1.5- Gain ratio¶
The Gain Ratio is a refinement of the Information Gain concept used in decision tree algorithms. It takes into account the intrinsic information of a split and helps to overcome some of the biases associated with Information Gain.
First split :¶
split our dataset into training (70%) and test (30%)
set.seed(1234)
ind <- sample(2, nrow(data), replace=TRUE, prob=c(0.70, 0.30))
trainData <- data[ind == 1, ]
testData <- data[ind == 2, ]
# Install and load the 'party' package
install.packages('party')
library(party)
# Define the formula
myFormula <- satisfaction ~ Inflight.entertainment + Seat.comfort + Ease.of.Online.booking
# Build the tree with a specified mincriterion value
satisfaction_ctree <- ctree(myFormula, data=trainData, controls = ctree_control(mincriterion = 0.99))
# Check the prediction
table(predict(satisfaction_ctree), trainData$satisfaction)
# Print the tree
print(satisfaction_ctree)
Installing package into ‘/srv/rlibs’ (as ‘lib’ is unspecified)
0 1
0 1766 272
1 304 1814
Conditional inference tree with 21 terminal nodes
Response: satisfaction
Inputs: Inflight.entertainment, Seat.comfort, Ease.of.Online.booking
Number of observations: 4156
1) Inflight.entertainment <= 3; criterion = 1, statistic = 1109.441
2) Ease.of.Online.booking <= 3; criterion = 1, statistic = 136.166
3) Inflight.entertainment <= 0; criterion = 1, statistic = 37.317
4) Seat.comfort <= 0; criterion = 1, statistic = 15.474
5)* weights = 33
4) Seat.comfort > 0
6)* weights = 18
3) Inflight.entertainment > 0
7) Seat.comfort <= 3; criterion = 1, statistic = 24.781
8) Seat.comfort <= 1; criterion = 0.992, statistic = 9.033
9) Seat.comfort <= 0; criterion = 1, statistic = 129.169
10)* weights = 11
9) Seat.comfort > 0
11) Inflight.entertainment <= 1; criterion = 0.998, statistic = 11.911
12)* weights = 192
11) Inflight.entertainment > 1
13) Ease.of.Online.booking <= 1; criterion = 0.993, statistic = 9.228
14)* weights = 27
13) Ease.of.Online.booking > 1
15)* weights = 28
8) Seat.comfort > 1
16)* weights = 876
7) Seat.comfort > 3
17) Seat.comfort <= 4; criterion = 0.993, statistic = 9.269
18)* weights = 41
17) Seat.comfort > 4
19)* weights = 11
2) Ease.of.Online.booking > 3
20) Seat.comfort <= 3; criterion = 1, statistic = 35
21) Seat.comfort <= 0; criterion = 1, statistic = 59.686
22)* weights = 36
21) Seat.comfort > 0
23)* weights = 524
20) Seat.comfort > 3
24) Seat.comfort <= 4; criterion = 1, statistic = 31.518
25) Inflight.entertainment <= 2; criterion = 1, statistic = 15.533
26)* weights = 49
25) Inflight.entertainment > 2
27)* weights = 70
24) Seat.comfort > 4
28)* weights = 58
1) Inflight.entertainment > 3
29) Ease.of.Online.booking <= 3; criterion = 1, statistic = 321.353
30) Inflight.entertainment <= 4; criterion = 1, statistic = 85.662
31) Ease.of.Online.booking <= 2; criterion = 1, statistic = 23.034
32)* weights = 262
31) Ease.of.Online.booking > 2
33)* weights = 190
30) Inflight.entertainment > 4
34)* weights = 162
29) Ease.of.Online.booking > 3
35) Inflight.entertainment <= 4; criterion = 1, statistic = 83.363
36) Seat.comfort <= 3; criterion = 1, statistic = 29.459
37)* weights = 354
36) Seat.comfort > 3
38) Seat.comfort <= 4; criterion = 1, statistic = 65.415
39)* weights = 374
38) Seat.comfort > 4
40)* weights = 134
35) Inflight.entertainment > 4
41)* weights = 706
# Build the tree with a specified maxdepth value
satisfaction_ctree <- ctree(myFormula, data = trainData, controls = ctree_control(maxdepth = 3))
# Plot the smaller tree
plot(satisfaction_ctree, type = "simple", extra = 1, under = TRUE)
In the first tree constructed using the Gain Ratio criterion with a 70-30 split, the 'inflight.entertainment' attribute serves as the root node. It classifies instances based on 'Ease.of.online.booking' values, dividing them into two branches: <=3 and >3.
For the branch where 'Ease.of.online.booking' is <=3, it further splits based on 'inflight.entertainment.' If it is also <=3, the tree then splits into two branches: <=0 and >0.
In the case of instances where 'Ease.of.online.booking' is >3, further classification is based on 'seat.comfort.' Instances with 'seat.comfort' >3 are divided into two branches: <=3 and >3.
Additionally, instances are further classified based on 'Ease.of.online.booking' and 'inflight.entertainment' values. When 'Ease.of.online.booking' is >3 and 'inflight.entertainment' is <=3, a split occurs based on the value <=4 or >4. Similarly, for 'Ease.of.online.booking' and 'inflight.entertainment' both >3, the split occurs based on <=4 or >4.
This tree construction process uses the Gain Ratio criterion to optimize the splitting of nodes, considering the information gain ratio and intrinsic information present in the attributes to create effective and balanced splits for classification.
# predict on test data
testPred <- predict(satisfaction_ctree, newdata = testData)
table(testPred, testData$satisfaction)
testPred 0 1
0 735 180
1 121 702
library(caret)
results <- confusionMatrix(testPred, testData$satisfaction)
acc <- results$overall["Accuracy"] * 100
acc
results
as.table(results)
as.matrix(results)
as.matrix(results, what = "overall")
as.matrix(results, what = "classes")
print(results)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 735 180
1 121 702
Accuracy : 0.8268
95% CI : (0.8082, 0.8443)
No Information Rate : 0.5075
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6539
Mcnemar's Test P-Value : 0.0008286
Sensitivity : 0.8586
Specificity : 0.7959
Pos Pred Value : 0.8033
Neg Pred Value : 0.8530
Prevalence : 0.4925
Detection Rate : 0.4229
Detection Prevalence : 0.5265
Balanced Accuracy : 0.8273
'Positive' Class : 0
Reference
Prediction 0 1
0 735 180
1 121 702
| 0 | 1 | |
|---|---|---|
| 0 | 735 | 180 |
| 1 | 121 | 702 |
| Accuracy | 8.268124e-01 |
|---|---|
| Kappa | 6.538989e-01 |
| AccuracyLower | 8.081869e-01 |
| AccuracyUpper | 8.443272e-01 |
| AccuracyNull | 5.074799e-01 |
| AccuracyPValue | 3.215444e-170 |
| McnemarPValue | 8.285866e-04 |
| Sensitivity | 0.8586449 |
|---|---|
| Specificity | 0.7959184 |
| Pos Pred Value | 0.8032787 |
| Neg Pred Value | 0.8529769 |
| Precision | 0.8032787 |
| Recall | 0.8586449 |
| F1 | 0.8300395 |
| Prevalence | 0.4925201 |
| Detection Rate | 0.4228999 |
| Detection Prevalence | 0.5264672 |
| Balanced Accuracy | 0.8272816 |
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 735 180
1 121 702
Accuracy : 0.8268
95% CI : (0.8082, 0.8443)
No Information Rate : 0.5075
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6539
Mcnemar's Test P-Value : 0.0008286
Sensitivity : 0.8586
Specificity : 0.7959
Pos Pred Value : 0.8033
Neg Pred Value : 0.8530
Prevalence : 0.4925
Detection Rate : 0.4229
Detection Prevalence : 0.5265
Balanced Accuracy : 0.8273
'Positive' Class : 0
Second split :¶
split our dataset into training (60%) and test (40%)
set.seed(1234)
ind=sample (2, nrow(data), replace=TRUE, prob=c(0.60 , 0.40))
trainData=data[ind==1,]
testData=data[ind==2,]
library(party)
# Define the formula
myFormula <- satisfaction ~ Inflight.entertainment + Seat.comfort + Ease.of.Online.booking
# Build the tree with a specified mincriterion value
satisfaction_ctree <- ctree(myFormula, data=trainData, controls = ctree_control(mincriterion = 0.99))
# Check the prediction
table(predict(satisfaction_ctree), trainData$satisfaction)
# Print the tree
print(satisfaction_ctree)
0 1
0 1500 239
1 265 1548
Conditional inference tree with 17 terminal nodes
Response: satisfaction
Inputs: Inflight.entertainment, Seat.comfort, Ease.of.Online.booking
Number of observations: 3552
1) Inflight.entertainment <= 3; criterion = 1, statistic = 933.927
2) Ease.of.Online.booking <= 3; criterion = 1, statistic = 122.821
3) Inflight.entertainment <= 0; criterion = 1, statistic = 32.386
4) Seat.comfort <= 0; criterion = 1, statistic = 15.448
5)* weights = 29
4) Seat.comfort > 0
6)* weights = 15
3) Inflight.entertainment > 0
7) Seat.comfort <= 3; criterion = 1, statistic = 20.842
8)* weights = 967
7) Seat.comfort > 3
9) Seat.comfort <= 4; criterion = 0.995, statistic = 9.862
10)* weights = 29
9) Seat.comfort > 4
11)* weights = 11
2) Ease.of.Online.booking > 3
12) Seat.comfort <= 3; criterion = 1, statistic = 31.203
13) Seat.comfort <= 0; criterion = 1, statistic = 59.334
14)* weights = 33
13) Seat.comfort > 0
15)* weights = 432
12) Seat.comfort > 3
16) Seat.comfort <= 4; criterion = 1, statistic = 26.524
17) Inflight.entertainment <= 2; criterion = 0.999, statistic = 12.127
18)* weights = 42
17) Inflight.entertainment > 2
19)* weights = 63
16) Seat.comfort > 4
20)* weights = 53
1) Inflight.entertainment > 3
21) Ease.of.Online.booking <= 3; criterion = 1, statistic = 278.272
22) Inflight.entertainment <= 4; criterion = 1, statistic = 66.391
23) Ease.of.Online.booking <= 2; criterion = 1, statistic = 21.462
24)* weights = 233
23) Ease.of.Online.booking > 2
25)* weights = 168
22) Inflight.entertainment > 4
26)* weights = 142
21) Ease.of.Online.booking > 3
27) Inflight.entertainment <= 4; criterion = 1, statistic = 69.4
28) Seat.comfort <= 3; criterion = 1, statistic = 25.346
29)* weights = 309
28) Seat.comfort > 3
30) Seat.comfort <= 4; criterion = 1, statistic = 59.843
31)* weights = 320
30) Seat.comfort > 4
32)* weights = 113
27) Inflight.entertainment > 4
33)* weights = 593
# Build the tree with a specified maxdepth value
satisfaction_ctree <- ctree(myFormula, data = trainData, controls = ctree_control(maxdepth = 3))
# Plot the smaller tree
plot(satisfaction_ctree, type = "simple", extra = 1, under = TRUE)
In second tree constructed using the Gain Ratio criterion with a 60-40 split, the 'inflight.entertainment' attribute acts as the root node. It categorizes instances into two branches based on 'Ease.of.online.booking': <=3 and >3.
For instances where 'Ease.of.online.booking' is <=3, further splitting occurs based on 'inflight.entertainment.' If it is also <=3, the tree splits into two branches: <=0 and >0 and result two different number of n and y .
When 'Ease.of.online.booking' is >3, the classification continues based on 'seat.comfort.' Instances with 'seat.comfort' >3 are divided into two branches: <=3 and >3 result two different number of n and y .
Additionally, instances undergo further classification based on 'Ease.of.online.booking' and 'inflight.entertainment' values. When 'Ease.of.online.booking' is >3 and 'inflight.entertainment' is <=3, the split occurs based on values <=4 or >4. Similarly, for 'Ease.of.online.booking' and 'inflight.entertainment' both >3, the split occurs based on <=4 or >4 result different number of n and y .
The Gain Ratio criterion ensures the tree constructs optimal splits, considering the information gain ratio and the intrinsic information of attributes, resulting in effective classification nodes.
# predict on test data
testPred <- predict(satisfaction_ctree, newdata = testData)
table(testPred, testData$satisfaction)
testPred 0 1
0 1005 235
1 156 946
library(caret)
results <- confusionMatrix(testPred, testData$satisfaction)
acc <- results$overall["Accuracy"] * 100
acc
results
as.table(results)
as.matrix(results)
as.matrix(results, what = "overall")
as.matrix(results, what = "classes")
print(results)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1005 235
1 156 946
Accuracy : 0.833
95% CI : (0.8173, 0.8479)
No Information Rate : 0.5043
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6663
Mcnemar's Test P-Value : 7.992e-05
Sensitivity : 0.8656
Specificity : 0.8010
Pos Pred Value : 0.8105
Neg Pred Value : 0.8584
Prevalence : 0.4957
Detection Rate : 0.4291
Detection Prevalence : 0.5295
Balanced Accuracy : 0.8333
'Positive' Class : 0
Reference
Prediction 0 1
0 1005 235
1 156 946
| 0 | 1 | |
|---|---|---|
| 0 | 1005 | 235 |
| 1 | 156 | 946 |
| Accuracy | 8.330487e-01 |
|---|---|
| Kappa | 6.662653e-01 |
| AccuracyLower | 8.173172e-01 |
| AccuracyUpper | 8.479422e-01 |
| AccuracyNull | 5.042699e-01 |
| AccuracyPValue | 8.301606e-243 |
| McnemarPValue | 7.992344e-05 |
| Sensitivity | 0.8656331 |
|---|---|
| Specificity | 0.8010161 |
| Pos Pred Value | 0.8104839 |
| Neg Pred Value | 0.8584392 |
| Precision | 0.8104839 |
| Recall | 0.8656331 |
| F1 | 0.8371512 |
| Prevalence | 0.4957301 |
| Detection Rate | 0.4291204 |
| Detection Prevalence | 0.5294620 |
| Balanced Accuracy | 0.8333246 |
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1005 235
1 156 946
Accuracy : 0.833
95% CI : (0.8173, 0.8479)
No Information Rate : 0.5043
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6663
Mcnemar's Test P-Value : 7.992e-05
Sensitivity : 0.8656
Specificity : 0.8010
Pos Pred Value : 0.8105
Neg Pred Value : 0.8584
Prevalence : 0.4957
Detection Rate : 0.4291
Detection Prevalence : 0.5295
Balanced Accuracy : 0.8333
'Positive' Class : 0
Third split :¶
split our dataset into training (85%) and test (15%)
set.seed(1234)
ind=sample (2, nrow(data), replace=TRUE, prob=c(0.85 , 0.15))
trainData=data[ind==1,]
testData=data[ind==2,]
library(party)
# Define the formula
myFormula <- satisfaction ~ Inflight.entertainment + Seat.comfort + Ease.of.Online.booking
# Build the tree with a specified mincriterion value
satisfaction_ctree <- ctree(myFormula, data=trainData, controls = ctree_control(mincriterion = 0.99))
# Check the prediction
table(predict(satisfaction_ctree), trainData$satisfaction)
# Print the tree
print(satisfaction_ctree)
0 1
0 2084 296
1 400 2225
Conditional inference tree with 23 terminal nodes
Response: satisfaction
Inputs: Inflight.entertainment, Seat.comfort, Ease.of.Online.booking
Number of observations: 5005
1) Inflight.entertainment <= 3; criterion = 1, statistic = 1359.345
2) Ease.of.Online.booking <= 3; criterion = 1, statistic = 147.866
3) Inflight.entertainment <= 0; criterion = 1, statistic = 41.549
4) Seat.comfort <= 0; criterion = 1, statistic = 14.591
5)* weights = 39
4) Seat.comfort > 0
6) Seat.comfort <= 1; criterion = 0.999, statistic = 12.526
7)* weights = 11
6) Seat.comfort > 1
8)* weights = 11
3) Inflight.entertainment > 0
9) Seat.comfort <= 3; criterion = 1, statistic = 35.701
10) Seat.comfort <= 1; criterion = 0.998, statistic = 11.333
11) Seat.comfort <= 0; criterion = 1, statistic = 109.593
12)* weights = 12
11) Seat.comfort > 0
13) Inflight.entertainment <= 1; criterion = 1, statistic = 22.568
14)* weights = 224
13) Inflight.entertainment > 1
15) Ease.of.Online.booking <= 1; criterion = 0.997, statistic = 11.126
16)* weights = 34
15) Ease.of.Online.booking > 1
17)* weights = 33
10) Seat.comfort > 1
18)* weights = 1061
9) Seat.comfort > 3
19) Seat.comfort <= 4; criterion = 0.998, statistic = 11.657
20)* weights = 51
19) Seat.comfort > 4
21)* weights = 15
2) Ease.of.Online.booking > 3
22) Seat.comfort <= 3; criterion = 1, statistic = 37.19
23) Seat.comfort <= 0; criterion = 1, statistic = 71.474
24)* weights = 40
23) Seat.comfort > 0
25)* weights = 633
22) Seat.comfort > 3
26) Seat.comfort <= 4; criterion = 1, statistic = 42.475
27) Inflight.entertainment <= 2; criterion = 1, statistic = 15.965
28)* weights = 56
27) Inflight.entertainment > 2
29)* weights = 84
26) Seat.comfort > 4
30)* weights = 68
1) Inflight.entertainment > 3
31) Ease.of.Online.booking <= 3; criterion = 1, statistic = 375.128
32) Inflight.entertainment <= 4; criterion = 1, statistic = 110.072
33) Ease.of.Online.booking <= 2; criterion = 1, statistic = 18.184
34) Seat.comfort <= 4; criterion = 0.994, statistic = 9.599
35)* weights = 289
34) Seat.comfort > 4
36)* weights = 14
33) Ease.of.Online.booking > 2
37)* weights = 226
32) Inflight.entertainment > 4
38)* weights = 195
31) Ease.of.Online.booking > 3
39) Inflight.entertainment <= 4; criterion = 1, statistic = 92.679
40) Seat.comfort <= 3; criterion = 1, statistic = 32.954
41)* weights = 426
40) Seat.comfort > 3
42) Seat.comfort <= 4; criterion = 1, statistic = 78.425
43)* weights = 445
42) Seat.comfort > 4
44)* weights = 168
39) Inflight.entertainment > 4
45)* weights = 870
# Build the tree with a specified maxdepth value
satisfaction_ctree <- ctree(myFormula, data = trainData, controls = ctree_control(maxdepth = 3))
# Plot the smaller tree
plot(satisfaction_ctree, type = "simple", extra = 1, under = TRUE)
For third tree constructed using the Gain Ratio criterion with an 85-15 split. This tree begins with the 'inflight.entertainment' attribute as the root node and branches based on 'Ease.of.online.booking' values.
For instances where 'Ease.of.online.booking' is <=3, further splitting occurs based on 'inflight.entertainment.' If it's also <=3, the tree branches into <=0 and >0, resulting in different counts for 'n' and 'y'.
When 'Ease.of.online.booking' is >3, the classification continues based on 'seat.comfort.' Instances with 'seat.comfort' >3 are divided into <=3 and >3 branches, also resulting in different counts for 'n' and 'y'.
Additionally, further classification based on 'Ease.of.online.booking' and 'inflight.entertainment' values leads to splits based on values <=4 or >4, resulting in varying counts for 'n' and 'y' in different branches.
The Gain Ratio criterion ensures that the tree optimally splits nodes by considering information gain ratios and the intrinsic information of attributes, thereby creating effective classification nodes.
# predict on test data
testPred <- predict(satisfaction_ctree, newdata = testData)
table(testPred, testData$satisfaction)
testPred 0 1
0 382 99
1 60 348
library(caret)
results <- confusionMatrix(testPred, testData$satisfaction)
acc <- results$overall["Accuracy"] * 100
acc
results
as.table(results)
as.matrix(results)
as.matrix(results, what = "overall")
as.matrix(results, what = "classes")
print(results)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 382 99
1 60 348
Accuracy : 0.8211
95% CI : (0.7943, 0.8458)
No Information Rate : 0.5028
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6425
Mcnemar's Test P-Value : 0.002582
Sensitivity : 0.8643
Specificity : 0.7785
Pos Pred Value : 0.7942
Neg Pred Value : 0.8529
Prevalence : 0.4972
Detection Rate : 0.4297
Detection Prevalence : 0.5411
Balanced Accuracy : 0.8214
'Positive' Class : 0
Reference
Prediction 0 1
0 382 99
1 60 348
| 0 | 1 | |
|---|---|---|
| 0 | 382 | 99 |
| 1 | 60 | 348 |
| Accuracy | 8.211474e-01 |
|---|---|
| Kappa | 6.424598e-01 |
| AccuracyLower | 7.943422e-01 |
| AccuracyUpper | 8.458038e-01 |
| AccuracyNull | 5.028121e-01 |
| AccuracyPValue | 5.612977e-87 |
| McnemarPValue | 2.581713e-03 |
| Sensitivity | 0.8642534 |
|---|---|
| Specificity | 0.7785235 |
| Pos Pred Value | 0.7941788 |
| Neg Pred Value | 0.8529412 |
| Precision | 0.7941788 |
| Recall | 0.8642534 |
| F1 | 0.8277356 |
| Prevalence | 0.4971879 |
| Detection Rate | 0.4296963 |
| Detection Prevalence | 0.5410574 |
| Balanced Accuracy | 0.8213884 |
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 382 99
1 60 348
Accuracy : 0.8211
95% CI : (0.7943, 0.8458)
No Information Rate : 0.5028
P-Value [Acc > NIR] : < 2.2e-16
Kappa : 0.6425
Mcnemar's Test P-Value : 0.002582
Sensitivity : 0.8643
Specificity : 0.7785
Pos Pred Value : 0.7942
Neg Pred Value : 0.8529
Prevalence : 0.4972
Detection Rate : 0.4297
Detection Prevalence : 0.5411
Balanced Accuracy : 0.8214
'Positive' Class : 0
6.1.6 - Analysis :¶
| training (70%) and test (30%) | training (60%) and test (40%) | training (85%) and test (15%) | |
|---|---|---|---|
| Accuracy | 0.8268 | 0.833 | 0.8211 |
| Precision | 0.803 | 0.810 | 0.794 |
| sensitivity | 0.858 | 0.865 | 0.864 |
| specificity | 0.795 | 0.801 | 0.778 |
The gain ratio, employed in decision tree algorithms for feature selection, represents the ratio of information gain to the intrinsic information of a split. Despite the absence of specific information gain values, the provided table encompass accuracy, precision, sensitivity, and specificity. While the accuracies exhibit proximity across all three scenario, the 60% training and 40% test split marginally outperforms with a slightly higher accuracy. This suggests a generally consistent overall model performance. Additionally, precision, sensitivity, and specificity values remain relatively consistent across the various splits, contributing to the model's robustness.
6.2 - Clustering :¶
clustering analysis can be a valuable technique to uncover inherent patterns and groupings among passenger satisfaction-related features. Utilizing clustering algorithms, such as K-means and Silhouette coefficient, also sum of square , BCubed precision and recall one can identify distinct clusters of passengers who share similar preferences, behaviors, or satisfaction levels. For instance, clustering might reveal groups of passengers with common characteristics like preferred seating options, in-flight services, or overall satisfaction scores
install.packages("multcomp")
library(multcomp)
install.packages('factoextra')
library(factoextra)
install.packages("tidyverse")
library(tidyverse)
Installing package into ‘/srv/rlibs’
(as ‘lib’ is unspecified)
Loading required package: mvtnorm
Loading required package: survival
Attaching package: ‘survival’
The following object is masked from ‘package:caret’:
cluster
Loading required package: TH.data
Loading required package: MASS
Attaching package: ‘MASS’
The following object is masked from ‘package:dplyr’:
select
Attaching package: ‘TH.data’
The following object is masked from ‘package:MASS’:
geyser
Installing package into ‘/srv/rlibs’
(as ‘lib’ is unspecified)
Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
Installing package into ‘/srv/rlibs’
(as ‘lib’ is unspecified)
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
✔ tibble 3.1.6 ✔ forcats 0.5.1
✔ purrr 0.3.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ randomForest::combine() masks dplyr::combine()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
✖ purrr::lift() masks caret::lift()
✖ randomForest::margin() masks ggplot2::margin()
✖ MASS::select() masks dplyr::select()
head(data)
| Gender | Customer.Type | Age | Type.of.Travel | Class | Seat.comfort | Departure.Arrival.time.convenient | Food.and.drink | Inflight.wifi.service | Inflight.entertainment | Online.support | Ease.of.Online.booking | On.board.service | Leg.room.service | Baggage.handling | Checkin.service | Cleanliness | Online.boarding | Arrival.Delay.in.Minutes | satisfaction | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <int> | <int> | <dbl> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <dbl> | <fct> | |
| 1 | 2 | 1 | 0.71232877 | 1 | 2 | 1 | 0 | 1 | 1 | 4 | 3 | 2 | 5 | 3 | 3 | 3 | 2 | 3 | 0.483974359 | 0 |
| 2 | 2 | 1 | 0.24657534 | 1 | 1 | 1 | 0 | 1 | 4 | 1 | 4 | 4 | 1 | 2 | 3 | 5 | 1 | 4 | 0.198717949 | 0 |
| 3 | 2 | 1 | 0.67123288 | 1 | 3 | 1 | 0 | 1 | 4 | 4 | 5 | 3 | 3 | 1 | 3 | 3 | 3 | 4 | 0.003205128 | 0 |
| 4 | 2 | 1 | 0.32876712 | 1 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 2 | 4 | 1 | 4 | 5 | 5 | 2 | 0.000000000 | 0 |
| 6 | 2 | 1 | 0.67123288 | 1 | 1 | 1 | 0 | 1 | 2 | 1 | 4 | 2 | 5 | 4 | 5 | 5 | 4 | 2 | 0.000000000 | 0 |
| 7 | 2 | 1 | 0.09589041 | 1 | 1 | 1 | 0 | 1 | 4 | 1 | 4 | 4 | 1 | 3 | 3 | 1 | 1 | 4 | 0.000000000 | 0 |
dataset<- data #in case we need the old data set(with the class label)
data <- data[,!names(data) %in% c("satisfaction")] # without the "satisfaction" column
data1 <- data[,!names(data) %in% c("satisfaction")]
data2 <- data[,!names(data) %in% c("satisfaction")]
head(data)
| Gender | Customer.Type | Age | Type.of.Travel | Class | Seat.comfort | Departure.Arrival.time.convenient | Food.and.drink | Inflight.wifi.service | Inflight.entertainment | Online.support | Ease.of.Online.booking | On.board.service | Leg.room.service | Baggage.handling | Checkin.service | Cleanliness | Online.boarding | Arrival.Delay.in.Minutes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| <int> | <int> | <dbl> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <int> | <dbl> | |
| 1 | 2 | 1 | 0.71232877 | 1 | 2 | 1 | 0 | 1 | 1 | 4 | 3 | 2 | 5 | 3 | 3 | 3 | 2 | 3 | 0.483974359 |
| 2 | 2 | 1 | 0.24657534 | 1 | 1 | 1 | 0 | 1 | 4 | 1 | 4 | 4 | 1 | 2 | 3 | 5 | 1 | 4 | 0.198717949 |
| 3 | 2 | 1 | 0.67123288 | 1 | 3 | 1 | 0 | 1 | 4 | 4 | 5 | 3 | 3 | 1 | 3 | 3 | 3 | 4 | 0.003205128 |
| 4 | 2 | 1 | 0.32876712 | 1 | 1 | 1 | 0 | 1 | 2 | 1 | 2 | 2 | 4 | 1 | 4 | 5 | 5 | 2 | 0.000000000 |
| 6 | 2 | 1 | 0.67123288 | 1 | 1 | 1 | 0 | 1 | 2 | 1 | 4 | 2 | 5 | 4 | 5 | 5 | 4 | 2 | 0.000000000 |
| 7 | 2 | 1 | 0.09589041 | 1 | 1 | 1 | 0 | 1 | 4 | 1 | 4 | 4 | 1 | 3 | 3 | 1 | 1 | 4 | 0.000000000 |
#Making changes for converting data types
#Converting interger&factor columns too numeric
data$Gender<- as.numeric(factor(data$Gender) )
data$Customer.Type <- as.numeric(factor(data$Customer.Type) )
data$Age <- data$Age
data$Type.of.Travel <- as.numeric(factor(data$Type.of.Travel) )
data$Class <- as.numeric(factor(data$Class) )
data$Seat.comfort <- as.numeric(factor(data$Seat.comfort) )
#data$Departure.Delay.in.Minutes <- data$Departure.Delay.in.Minutes
data$Departure.Arrival.time.convenient <- data$Departure.Arrival.time.convenient
data$Inflight.wifi.service <- data$Inflight.wifi.service
data$Inflight.entertainment <- as.numeric(factor(data$Inflight.entertainment))
data$Ease.of.Online.booking <- as.numeric(factor(data$Ease.of.Online.booking) )
data$Online.support <- as.numeric(factor(data$Online.support) )
data$Food.and.drink <- as.numeric(factor(data$Food.and.drink))
data$Arrival.Delay.in.Minutes <-data$Arrival.Delay.in.Minutes
data$On.board.service <- as.numeric(factor(data$On.board.service))
data$Leg.room.service <- as.numeric(factor(data$Leg.room.service))
data$Baggage.handling <- as.numeric(factor(data$Baggage.handling))
data$Checkin.service <- as.numeric(factor(data$Checkin.service))
data$Cleanliness <- as.numeric(factor(data$Cleanliness))
data$Online.boarding <- as.numeric(factor(data$Online.boarding))
str(data)
'data.frame': 5894 obs. of 19 variables: $ Gender : num 2 2 2 2 2 2 2 1 2 2 ... $ Customer.Type : num 1 1 1 1 1 1 1 1 1 1 ... $ Age : num 0.712 0.247 0.671 0.329 0.671 ... $ Type.of.Travel : num 1 1 1 1 1 1 1 1 1 1 ... $ Class : num 2 1 3 1 1 1 1 2 1 2 ... $ Seat.comfort : num 2 2 2 2 2 2 2 2 2 2 ... $ Departure.Arrival.time.convenient: int 0 0 0 0 0 0 1 1 1 1 ... $ Food.and.drink : num 2 2 2 2 2 2 1 2 2 2 ... $ Inflight.wifi.service : int 1 4 4 2 2 4 3 1 4 2 ... $ Inflight.entertainment : num 5 2 5 2 2 2 1 4 2 2 ... $ Online.support : num 3 4 5 2 4 4 3 3 4 4 ... $ Ease.of.Online.booking : num 3 5 4 3 3 5 4 3 5 3 ... $ On.board.service : num 6 2 4 5 6 2 3 2 2 3 ... $ Leg.room.service : num 4 3 2 2 5 4 3 2 6 6 ... $ Baggage.handling : num 3 3 3 4 5 3 2 1 3 2 ... $ Checkin.service : num 3 5 3 5 5 1 1 3 3 4 ... $ Cleanliness : num 3 2 4 6 5 2 3 4 4 4 ... $ Online.boarding : num 4 5 5 3 3 5 4 4 5 3 ... $ Arrival.Delay.in.Minutes : num 0.48397 0.19872 0.00321 0 0 ...
library(cluster)
##elbow function
wssplot<- function (data, nc=15 ,seed=1234)
{
wss<-(nrow(dataset)-1)*sum(apply(dataset,2,var))
for(i in 2:nc){
set.seed(seed)
wss[i] <- sum(kmeans(dataset,centers = i)$withinss)}
plot(1:nc,wss, type="b", xlab="number of clusters", ylab="within groups sum of squares")
}
dataset =na.omit(dataset)
selected_data <- dataset
selected_data <- dataset
wssplot(selected_data)
wssplot(selected_data)
wssplot(selected_data)
The elbow method offers a visual guide for determining the optimal number of clusters in a clustering analysis. It aids in finding a balance between keeping the model simple and ensuring that it captures meaningful patterns within the dataset. By identifying the elbow point on the plot of within-cluster sum of squares against the number of clusters, the method assists in making an informed decision about the most suitable clustering configuration which is 4 , contributing to the effective interpretation of underlying structures in the data.
6.2.1- cluster k=2 :¶
#calculate k-mean k=2
km <- kmeans(data, 2, iter.max = 140 , algorithm="Lloyd", nstart=100)
km
K-means clustering with 2 clusters of sizes 3435, 2459
Cluster means:
Gender Customer.Type Age Type.of.Travel Class Seat.comfort
1 1.468122 1.142358 0.4525413 1.692868 2.134207 4.178748
2 1.562017 1.281822 0.4215880 1.689305 1.793412 3.305815
Departure.Arrival.time.convenient Food.and.drink Inflight.wifi.service
1 3.082969 4.031732 3.836681
2 2.824725 3.555917 2.324522
Inflight.entertainment Online.support Ease.of.Online.booking On.board.service
1 4.883261 4.207569 5.271033 4.856769
2 3.536397 2.485563 3.262302 3.809679
Leg.room.service Baggage.handling Checkin.service Cleanliness Online.boarding
1 4.827365 4.006405 3.607569 5.010771 5.055604
2 3.932086 3.187881 2.845872 4.210248 3.281009
Arrival.Delay.in.Minutes
1 0.02153594
2 0.02620541
Clustering vector:
1 2 3 4 6 7 8 10 11 12 13 14 18
2 2 2 2 2 2 2 2 2 2 2 2 2
19 20 21 22 24 25 28 29 31 32 33 34 36
2 2 2 2 2 2 2 1 2 2 2 2 1
37 39 42 45 46 47 49 50 53 54 55 56 60
2 2 2 2 2 2 2 2 2 1 2 2 2
62 63 69 70 71 73 74 75 76 81 82 83 85
2 1 2 2 2 1 2 1 2 2 2 2 2
88 89 90 92 93 94 96 97 98 100 101 104 105
2 1 1 2 2 2 2 2 1 1 1 2 2
106 107 108 109 112 113 114 115 122 125 126 127 128
1 2 1 2 1 2 1 1 2 2 2 2 2
129 130 134 135 136 139 142 147 148 149 151 152 155
2 1 1 2 2 1 2 2 2 2 1 1 1
160 161 162 163 165 168 169 170 171 173 175 176 177
2 2 2 1 1 1 2 2 2 2 2 1 2
178 183 184 185 186 188 189 196 197 199 203 205 209
2 2 1 1 1 1 2 1 2 1 2 2 2
211 212 213 217 221 222 223 226 227 232 234 238 239
2 2 1 2 2 1 2 2 1 2 1 2 2
241 242 245 247 249 251 252 254 255 257 259 261 262
2 2 1 2 2 2 2 2 2 2 1 2 2
264 267 271 273 274 275 276 278 281 283 285 286 287
1 2 2 1 2 2 2 1 2 2 1 2 2
288 289 290 291 292 293 295 297 299 301 303 307 308
1 2 2 2 2 1 2 2 2 2 2 2 2
310 311 317 319 320 322 324 325 326 328 330 332 334
1 2 2 2 2 2 2 2 2 2 2 2 2
335 337 338 341 342 346 348 349 351 354 356 358 359
1 1 1 2 2 2 2 2 2 2 2 2 2
361 362 363 364 366 369 372 373 375 376 379 380 381
1 2 1 2 2 1 1 1 2 2 1 2 2
382 384 386 387 388 390 391 394 398 400 401 402 406
2 2 2 1 2 1 2 2 1 2 2 2 1
407 408 409 411 413 414 416 421 423 430 431 432 433
2 2 1 1 1 1 2 1 2 2 1 2 2
435 436 438 440 442 444 445 448 451 453 455 456 459
1 2 2 1 1 1 2 2 2 1 1 1 1
462 463 464 465 466 467 468 475 476 477 480 481 483
1 2 2 2 1 2 2 2 1 2 1 2 1
484 487 490 492 495 496 498 499 500 503 504 505 508
2 1 2 1 2 2 2 1 2 1 2 1 2
511 515 516 519 521 524 526 527 530 532 533 536 537
2 2 2 1 1 2 1 2 2 2 1 2 1
538 539 543 544 545 547 551 552 553 554 556 558 559
2 2 1 2 2 1 2 2 2 1 2 2 2
560 562 564 565 569 571 572 573 575 579 581 586 587
1 1 1 2 2 2 1 1 1 1 1 2 2
589 592 593 595 596 597 602 604 605 607 609 614 615
1 2 2 2 2 1 2 1 2 1 1 2 2
617 619 620 622 626 631 635 636 638 640 641 643 646
1 2 1 2 2 2 1 2 2 2 2 2 2
647 648 649 651 653 655 656 657 658 659 660 662 663
2 1 1 2 2 1 2 2 1 1 1 2 2
664 666 667 669 670 673 675 677 679 684 686 687 688
2 2 2 1 1 2 2 2 1 2 2 2 2
691 692 693 694 696 701 706 707 708 711 712 715 717
2 2 2 2 2 2 2 1 2 2 2 2 1
718 720 721 722 724 725 727 728 729 730 734 735 736
1 2 2 2 1 2 1 1 1 1 1 1 2
737 738 739 740 741 742 743 744 745 746 747 748 749
2 1 1 2 1 1 1 1 1 1 2 1 2
751 752 753 756 757 758 759 761 762 764 765 766 767
1 2 2 1 2 1 2 2 2 2 2 1 2
768 769 771 776 777 779 781 782 784 787 788 794 795
2 1 1 1 2 2 2 1 2 2 2 1 2
796 802 803 804 806 809 814 815 818 819 821 822 823
1 2 1 1 2 1 1 2 2 1 1 2 2
825 826 828 830 831 832 833 835 836 842 843 847 848
2 1 2 2 2 2 1 2 2 2 2 2 2
850 851 853 858 859 860 864 867 869 870 872 874 877
2 2 1 2 2 2 2 2 2 2 2 1 2
880 881 883 886 887 888 889 892 893 894 897 903 904
2 2 2 2 1 2 1 2 2 1 1 2 2
905 906 907 908 909 912 913 914 916 917 919 922 923
2 1 1 1 1 1 1 1 1 2 2 2 2
924 925 927 929 931 932 933 938 940 941 942 944 945
2 2 2 2 1 1 1 1 1 1 2 1 2
947 949 952 954 955 956 958 961 963 964 969 970 972
1 2 1 1 2 2 1 1 2 2 1 2 1
973 974 977 978 979 980 982 983 985 986 987 988 990
1 2 1 1 1 1 2 2 2 2 1 2 2
992 993 994 997 1000 1005 1007 1011 1015 1016 1018 1019 1021
2 2 1 1 2 1 1 1 1 1 2 1 1
1022 1023 1024 1025 1027 1028 1031 1032 1036 1037 1038 1039 1043
1 1 2 1 1 2 2 2 1 1 2 1 2
1048 1049 1050 1051 1052 1053 1054 1055 1057 1058 1061 1062 1063
2 1 2 2 1 1 1 2 1 1 1 2 1
1065 1066 1070 1071 1072 1076 1079 1080 1081 1082 1088 1091 1093
2 1 2 2 1 1 2 1 1 2 1 2 2
1095 1096 1098 1099 1101 1104 1109 1110 1112 1114 1115 1116 1118
2 2 1 1 2 2 1 1 1 2 1 2 1
1119 1120 1121 1122 1123 1125 1127 1133 1134 1135 1136 1137 1138
1 1 1 2 2 2 1 2 1 1 1 1 2
1140 1141 1142 1145 1146 1148 1151 1153 1155 1156 1158 1159 1160
2 2 1 1 1 2 2 1 2 1 2 1 2
1161 1162 1163 1164 1166 1168 1169 1170 1173 1174 1181 1182 1189
2 2 2 1 1 2 1 1 1 1 1 1 2
1191 1192 1193 1194 1201 1202 1203 1204 1210 1212 1213 1214 1221
1 2 2 1 1 1 1 1 1 1 2 2 2
1223 1224 1225 1226 1227 1228 1229 1230 1231 1236 1238 1240 1242
2 1 2 1 2 2 1 1 2 1 2 1 1
1243 1244 1246 1247 1249 1250 1251 1252 1253 1254 1256 1257 1259
1 2 1 1 2 2 2 2 2 2 2 1 2
1261 1262 1263 1264 1267 1268 1270 1271 1272 1273 1274 1275 1276
1 2 2 2 1 2 2 2 1 2 2 1 1
1277 1280 1281 1282 1283 1285 1287 1288 1289 1290 1291 1292 1293
1 1 1 1 2 2 2 1 2 2 1 2 2
1295 1297 1298 1301 1304 1307 1313 1316 1317 1319 1320 1324 1325
2 2 1 1 1 2 1 2 2 1 2 2 1
1326 1330 1334 1340 1342 1344 1347 1349 1350 1353 1354 1356 1358
2 1 1 2 1 1 2 2 1 1 2 1 1
1359 1360 1361 1362 1364 1365 1369 1370 1373 1374 1376 1378 1383
2 1 1 2 1 1 1 1 1 1 2 1 1
1385 1386 1387 1388 1393 1394 1397 1400 1403 1406 1408 1409 1412
1 1 1 2 2 1 2 1 1 1 1 2 2
1417 1418 1420 1421 1422 1424 1425 1429 1430 1431 1433 1434 1436
1 1 2 1 2 1 1 1 1 2 2 2 1
1437 1439 1440 1443 1444 1445 1447 1449 1451 1453 1460 1461 1463
1 2 1 2 2 2 1 2 2 2 1 1 1
1464 1466 1467 1468 1470 1471 1472 1473 1474 1476 1478 1479 1483
2 1 2 1 1 1 1 2 1 2 2 1 1
1486 1490 1491 1492 1495 1496 1497 1498 1500 1503 1504 1505 1507
1 2 1 1 1 1 2 2 1 1 1 2 1
1509 1510 1511 1514 1516 1517 1518 1519 1526 1528 1531 1533 1537
1 1 1 2 1 1 1 2 1 1 2 1 2
1538 1539 1541 1545 1547 1548 1549 1554 1555 1556 1561 1564 1566
1 1 1 2 1 1 1 2 2 1 2 2 2
1567 1569 1570 1571 1572 1574 1575 1576 1577 1578 1579 1580 1582
1 1 2 2 2 2 2 2 2 2 2 2 1
1587 1588 1589 1590 1592 1593 1594 1595 1596 1598 1599 1603 1604
1 2 2 1 1 1 1 2 1 1 1 2 2
1606 1607 1609 1610 1611 1612 1614 1616 1618 1619 1623 1624 1625
2 2 2 2 2 2 2 2 2 1 2 1 2
1626 1629 1630 1632 1634 1636 1637 1638 1639 1641 1642 1643 1646
2 2 2 2 2 2 2 2 2 1 2 1 2
1647 1648 1649 1650 1656 1659 1660 1661 1663 1665 1667 1669 1670
2 2 2 1 2 2 2 2 2 1 2 2 1
1671 1672 1673 1676 1677 1679 1681 1682 1683 1689 1690 1692 1693
1 2 2 1 1 2 1 2 2 2 2 2 2
1694 1696 1698 1699 1700 1707 1708 1709 1712 1714 1718 1719 1722
2 2 2 1 2 2 2 1 1 2 2 1 2
1723 1724 1729 1732 1733 1734 1735 1736 1737 1740 1741 1742 1744
1 2 2 1 2 2 2 1 1 1 2 2 2
1747 1748 1749 1752 1753 1754 1755 1756 1758 1759 1760 1763 1767
2 2 2 1 1 2 1 2 2 1 2 2 2
1768 1770 1771 1772 1775 1777 1778 1781 1782 1783 1785 1786 1795
2 2 2 2 2 2 2 1 2 2 2 2 2
1797 1798 1799 1800 1801 1802 1805 1806 1807 1809 1810 1812 1813
2 1 2 2 2 2 2 2 2 2 2 2 1
1814 1816 1817 1821 1823 1824 1825 1827 1828 1830 1831 1833 1835
2 2 2 1 2 2 2 2 1 2 2 2 2
1837 1838 1841 1842 1846 1847 1848 1849 1850 1851 1853 1854 1855
2 1 2 2 1 2 2 1 2 2 2 1 2
1858 1860 1862 1868 1869 1871 1874 1875 1876 1878 1879 1881 1882
1 1 1 2 1 2 2 2 2 1 1 2 2
1883 1884 1889 1890 1892 1893 1895 1896 1899 1901 1903 1904 1905
2 2 1 2 1 1 2 2 1 1 2 1 2
1906 1907 1910 1913 1914 1917 1919 1921 1926 1928 1929 1931 1933
1 2 2 2 1 2 1 2 2 1 2 1 2
1934 1935 1936 1939 1941 1942 1943 1944 1945 1947 1948 1950 1955
2 2 2 2 2 2 2 2 2 1 1 1 2
1959 1960 1961 1962 1964 1966 1967 1971 1972 1973 1974 1975 1976
2 2 2 1 2 2 1 2 2 1 1 1 2
1978 1979 1981 1982 1984 1986 1988 1989 1990 1994 1996 1997 1999
1 1 2 2 2 2 2 2 1 1 1 2 2
2000 2001 2002 2003 2006 2007 2009 2011 2015 2019 2020 2021 2022
2 1 2 1 1 2 1 2 2 2 2 1 2
2023 2026 2027 2029 2031 2032 2034 2035 2036 2038 2041 2042 2043
2 1 2 2 2 1 2 2 1 2 2 2 2
2045 2046 2047 2048 2050 2051 2053 2054 2055 2057 2059 2060 2061
1 2 1 1 2 2 2 2 2 2 2 1 2
2062 2063 2065 2066 2068 2069 2070 2071 2072 2073 2074 2076 2077
2 2 2 1 2 2 1 2 1 2 1 1 2
2080 2083 2084 2085 2086 2088 2089 2090 2092 2094 2095 2096 2098
1 2 2 2 2 1 2 2 2 1 1 2 2
2100 2105 2110 2112 2115 2117 2119 2121 2123 2124 2126 2129 2132
2 1 1 2 2 2 2 2 2 1 2 1 2
2133 2135 2136 2137 2138 2140 2141 2142 2145 2147 2148 2149 2150
2 1 2 1 2 1 1 2 1 1 1 2 2
2151 2152 2153 2154 2155 2156 2157 2161 2163 2166 2168 2173 2177
2 1 2 2 2 2 2 2 2 2 2 1 1
2181 2182 2184 2185 2188 2189 2190 2191 2192 2195 2196 2198 2201
2 1 2 2 2 2 2 1 1 1 1 2 2
2202 2209 2210 2211 2212 2215 2216 2220 2225 2227 2229 2230 2231
2 2 2 1 2 1 2 2 1 2 2 2 2
2232 2235 2237 2241 2243 2244 2245 2246 2247 2249 2250 2252 2254
2 1 1 1 2 2 1 2 1 1 1 2 2
2255 2256 2258 2259 2260 2261 2265 2269 2271 2274 2275 2276 2279
2 1 2 2 1 2 2 2 1 2 1 2 1
2280 2281 2282 2284 2285 2288 2289 2290 2297 2299 2301 2303 2304
1 2 1 1 1 2 2 2 1 2 2 1 2
2305 2307 2309 2310 2311 2312 2314 2316 2317 2318 2320 2321 2322
1 1 2 2 1 1 2 2 2 2 2 2 2
2323 2328 2331 2333 2334 2335 2337 2341 2342 2344 2345 2346 2347
2 2 1 2 1 1 2 2 2 2 2 2 2
2348 2349 2351 2352 2354 2355 2356 2357 2358 2359 2363 2364 2365
2 2 1 1 2 2 1 2 1 2 2 1 1
2366 2368 2369 2370 2373 2375 2376 2377 2378 2382 2383 2388 2389
1 1 1 2 2 2 1 2 2 1 1 1 1
2390 2393 2394 2395 2397 2398 2401 2403 2404 2407 2408 2409 2411
2 1 2 1 2 2 2 2 2 2 1 2 1
2413 2414 2415 2417 2419 2421 2422 2424 2425 2426 2427 2429 2430
2 2 2 2 2 2 1 1 2 2 1 2 2
2431 2432 2433 2440 2441 2442 2443 2446 2447 2450 2451 2454 2456
1 2 1 1 1 2 2 1 2 2 2 2 1
2457 2458 2460 2464 2465 2467 2470 2473 2474 2476 2479 2480 2481
1 1 1 1 1 1 1 2 1 2 2 2 2
2482 2484 2490 2493 2494 2495 2502 2505 2506 2509 2510 2511 2513
2 1 1 1 2 1 1 1 2 1 2 2 1
2514 2516 2518 2519 2520 2524 2532 2536 2537 2540 2541 2542 2543
2 2 2 1 2 2 2 2 1 2 2 1 2
2544 2546 2547 2548 2553 2554 2558 2559 2560 2561 2562 2566 2567
1 2 2 1 2 1 2 1 1 1 1 1 2
2568 2569 2571 2572 2575 2576 2577 2578 2580 2582 2587 2588 2589
2 2 2 2 2 2 1 2 2 2 1 1 2
2590 2591 2592 2596 2598 2599 2600 2603 2607 2608 2611 2614 2618
2 1 2 1 2 2 2 2 1 1 1 2 2
2619 2620 2623 2625 2626 2627 2628 2629 2631 2633 2636 2638 2639
1 2 1 2 1 1 1 2 2 1 2 1 2
2640 2642 2643 2646 2649 2650 2651 2652 2653 2654 2655 2656 2662
1 2 1 2 1 2 2 2 2 2 2 2 2
2668 2670 2671 2672 2673 2675 2677 2680 2682 2683 2686 2688 2691
2 2 2 2 2 1 2 1 1 2 1 2 1
2692 2693 2694 2696 2697 2698 2699 2700 2704 2705 2708 2709 2710
2 1 1 1 1 2 1 2 1 1 2 2 1
2711 2712 2713 2715 2716 2717 2720 2722 2723 2725 2726 2727 2737
2 2 1 1 2 2 1 2 1 2 2 1 2
2740 2741 2742 2747 2748 2749 2750 2753 2757 2758 2760 2763 2764
2 2 2 2 2 2 1 2 2 2 2 1 2
2765 2767 2768 2769 2771 2777 2779 2786 2788 2789 2790 2791 2792
1 1 2 2 1 1 2 2 1 2 2 1 1
2794 2795 2796 2797 2799 2800 2801 2803 2807 2808 2810 2812 2817
2 1 1 1 1 2 1 1 1 2 1 1 1
2818 2820 2822 2824 2825 2826 2827 2829 2830 2831 2835 2836 2837
1 1 2 2 1 2 1 1 2 1 1 1 1
2839 2841 2842 2843 2844 2846 2847 2848 2849 2852 2854 2855 2856
2 2 2 2 1 2 2 2 2 2 1 2 1
2858 2860 2863 2865 2866 2867 2874 2877 2878 2880 2882 2887 2888
1 2 2 1 2 1 1 2 1 2 1 2 1
2890 2892 2894 2895 2898 2900 2901 2905 2907 2909 2910 2911 2915
1 1 1 2 1 2 1 1 2 2 2 2 2
2917 2920 2922 2924 2925 2926 2930 2932 2935 2936 2942 2943 2944
1 1 1 1 2 2 2 1 2 1 1 1 1
2946 2947 2950 2952 2953 2954 2957 2958 2960 2962 2964 2965 2969
1 2 1 1 1 1 1 1 1 1 2 1 1
2975 2979 2980 2981 2983 2985 2986 2988 2990 2994 2996 2997 2999
2 2 2 2 2 2 2 2 2 2 2 2 2
3003 3006 3008 3010 3012 3013 3018 3023 3024 3025 3026 3027 3028
2 2 2 2 2 2 2 2 2 2 2 2 2
3030 3033 3034 3036 3038 3039 3043 3044 3045 3047 3049 3051 3052
2 2 2 2 2 2 2 2 2 2 2 2 2
3053 3055 3056 3058 3059 3062 3064 3065 3068 3069 3070 3071 3072
2 2 2 2 2 2 2 2 2 2 2 2 2
3073 3075 3077 3078 3083 3086 3093 3095 3097 3098 3099 3101 3103
2 2 2 2 2 2 2 2 2 2 2 2 2
3110 3111 3112 3113 3114 3115 3116 3118 3119 3121 3122 3123 3125
2 2 2 2 2 2 2 2 2 2 2 2 2
3127 3128 3130 3132 3133 3134 3136 3137 3138 3139 3140 3142 3143
2 2 2 2 2 2 2 2 2 2 2 2 2
3145 3146 3150 3152 3155 3159 3161 3167 3169 3178 3179 3181 3182
2 2 2 2 2 2 2 2 2 2 2 2 2
3184 3185 3187 3189 3190 3193 3194 3196 3197 3198 3200 3201 3202
2 2 2 2 2 2 2 2 2 2 2 2 2
3204 3205 3209 3211 3212 3214 3216 3217 3220 3222 3223 3224 3226
2 2 2 2 2 2 2 2 2 2 2 2 2
3229 3230 3231 3233 3236 3237 3239 3247 3248 3249 3250 3252 3255
2 2 2 2 2 2 2 2 2 2 2 2 2
3256 3260 3262 3265 3266 3267 3269 3271 3272 3273 3274 3276 3282
2 2 2 2 2 2 2 2 2 2 2 2 2
3285 3287 3291 3294 3297 3298 3299 3300 3301 3302 3303 3305 3309
2 2 2 2 2 2 2 2 2 2 2 2 2
3311 3315 3317 3318 3321 3322 3323 3326 3327 3328 3329 3331 3334
2 2 2 2 2 2 2 2 2 2 2 2 2
3337 3338 3341 3343 3347 3348 3350 3355 3357 3358 3364 3370 3372
2 2 2 2 2 2 2 2 2 2 2 2 2
3373 3374 3376 3380 3381 3382 3383 3389 3391 3394 3395 3396 3403
2 2 2 2 2 2 2 2 2 2 2 2 2
3407 3408 3409 3411 3413 3414 3415 3418 3419 3420 3422 3423 3427
2 2 2 2 2 2 2 2 2 2 2 2 2
3431 3432 3433 3434 3438 3441 3444 3448 3450 3452 3453 3455 3456
2 2 2 2 2 2 2 2 2 2 2 2 2
3457 3458 3459 3462 3464 3465 3467 3469 3470 3473 3474 3476 3477
2 2 2 2 2 2 2 2 2 2 2 2 2
3479 3480 3481 3482 3483 3484 3487 3490 3494 3495 3497 3498 3499
2 2 2 2 2 2 2 2 2 2 2 2 2
3500 3502 3503 3504 3505 3510 3511 3512 3515 3516 3517 3518 3521
2 2 2 2 2 2 2 2 2 2 2 2 2
3523 3524 3525 3528 3529 3531 3532 3537 3538 3540 3543 3545 3546
2 2 2 2 2 2 2 2 2 2 2 2 2
3548 3549 3551 3552 3555 3556 3557 3558 3560 3563 3564 3566 3567
2 2 2 2 2 2 2 2 2 2 2 2 2
3568 3570 3571 3573 3575 3576 3577 3578 3579 3580 3584 3585 3587
2 2 2 2 2 2 2 2 2 2 2 2 2
3588 3589 3590 3591 3601 3604 3605 3607 3608 3609 3610 3611 3612
2 2 2 2 2 2 2 2 2 2 2 2 2
3614 3616 3617 3618 3624 3625 3627 3628 3631 3634 3635 3637 3640
2 2 2 2 2 2 2 2 2 2 2 2 2
3641 3643 3644 3645 3646 3647 3648 3653 3654 3655 3658 3661 3663
2 2 2 2 2 2 2 2 2 2 2 2 2
3664 3666 3668 3669 3671 3672 3673 3675 3677 3679 3680 3684 3685
2 2 2 2 2 2 2 2 2 2 2 2 2
3686 3692 3693 3695 3696 3698 3699 3700 3701 3702 3703 3707 3709
2 2 2 2 2 2 2 2 1 2 2 2 2
3710 3712 3713 3714 3715 3716 3718 3719 3720 3721 3726 3731 3735
2 2 2 2 2 2 2 2 2 2 2 1 2
3737 3739 3741 3742 3745 3746 3748 3752 3753 3754 3755 3756 3757
2 2 2 2 2 2 2 2 2 2 2 2 2
3760 3761 3762 3764 3767 3771 3772 3781 3784 3787 3788 3790 3791
2 2 2 2 2 2 2 2 2 2 2 2 2
3793 3795 3796 3797 3801 3802 3804 3805 3807 3810 3811 3815 3817
2 2 2 2 2 2 2 2 2 2 2 2 2
3818 3822 3823 3825 3826 3830 3831 3832 3834 3835 3836 3838 3839
2 2 2 2 2 2 2 2 2 2 1 1 2
3840 3845 3847 3848 3849 3850 3851 3852 3853 3854 3857 3858 3859
2 2 2 2 2 2 2 2 2 2 2 2 2
3860 3861 3863 3867 3868 3873 3875 3876 3877 3881 3882 3883 3885
2 1 2 2 2 2 2 2 1 2 2 2 2
3886 3887 3888 3890 3891 3892 3893 3896 3898 3901 3904 3905 3907
2 1 2 2 2 1 2 2 1 2 2 2 2
3908 3913 3915 3916 3921 3923 3924 3926 3928 3931 3932 3934 3935
2 2 2 2 2 2 1 2 2 2 2 2 2
3941 3942 3943 3944 3952 3953 3954 3957 3958 3959 3963 3964 3965
1 2 2 2 2 1 2 2 2 2 2 2 2
3967 3970 3973 3974 3975 3977 3978 3979 3982 3983 3985 3986 3987
2 2 2 2 2 2 2 2 2 2 1 2 1
3988 3989 3994 3996 3998 3999 4000 4001 4004 4005 4007 4010 4013
2 1 2 1 2 2 1 2 2 2 2 2 2
4016 4017 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029
2 2 2 2 2 2 2 2 2 2 2 2 2
4031 4034 4037 4039 4041 4043 4044 4047 4049 4050 4051 4053 4058
2 2 2 2 2 2 2 2 2 1 2 2 2
4063 4064 4066 4071 4073 4074 4076 4077 4078 4080 4082 4086 4087
2 2 2 2 2 2 1 2 2 2 2 1 2
4088 4089 4090 4091 4092 4093 4094 4095 4098 4099 4104 4105 4106
2 2 2 2 2 2 2 2 2 2 1 2 2
4107 4108 4109 4111 4112 4114 4116 4119 4120 4121 4124 4125 4128
2 2 1 1 2 2 2 2 2 2 2 2 2
4129 4131 4132 4134 4137 4139 4141 4144 4145 4146 4147 4148 4151
2 2 2 2 2 2 2 2 2 2 1 2 2
4152 4154 4155 4156 4157 4162 4166 4167 4169 4170 4171 4172 4173
1 2 2 2 1 1 2 2 2 2 2 2 1
4175 4177 4178 4180 4181 4184 4185 4186 4190 4194 4197 4198 4200
1 2 2 1 1 1 1 1 1 2 1 1 2
4204 4205 4207 4210 4212 4213 4216 4217 4220 4221 4223 4226 4230
1 1 2 2 1 1 2 2 1 1 1 1 2
4231 4240 4241 4242 4244 4245 4249 4250 4251 4254 4255 4256 4257
2 1 2 1 2 1 1 2 1 2 1 2 1
4259 4260 4261 4269 4271 4272 4274 4276 4277 4278 4280 4282 4283
1 1 1 1 1 1 1 1 1 1 1 1 1
4287 4288 4289 4290 4291 4293 4294 4295 4297 4298 4301 4303 4305
1 1 2 1 1 1 1 2 1 1 1 2 2
4306 4308 4309 4310 4313 4314 4315 4316 4317 4318 4320 4321 4323
1 1 1 1 1 1 1 1 2 1 1 1 1
4325 4326 4328 4329 4330 4332 4333 4336 4337 4339 4341 4342 4343
1 1 2 1 1 1 1 1 1 1 1 1 1
4344 4347 4352 4353 4356 4357 4358 4359 4361 4362 4363 4367 4368
1 1 1 1 1 1 1 1 1 2 1 1 1
4369 4370 4371 4372 4375 4376 4378 4379 4380 4382 4383 4384 4386
1 2 1 1 1 1 1 1 1 1 1 1 1
4387 4388 4390 4391 4392 4394 4395 4400 4401 4404 4405 4406 4409
1 1 1 1 1 1 1 1 1 1 1 1 1
4411 4413 4415 4416 4422 4423 4427 4430 4431 4432 4434 4435 4438
1 1 2 2 2 2 2 2 2 2 1 1 2
4439 4441 4444 4446 4447 4449 4450 4451 4452 4454 4455 4456 4459
1 2 1 2 1 2 2 2 1 1 2 2 2
4460 4461 4463 4468 4474 4475 4476 4479 4481 4484 4485 4488 4489
2 1 2 1 2 2 2 2 1 2 1 2 1
4491 4493 4494 4499 4500 4501 4502 4503 4504 4506 4507 4509 4510
2 2 2 1 2 1 1 2 2 1 2 1 2
4511 4514 4516 4517 4518 4528 4531 4532 4533 4537 4538 4540 4542
2 1 2 2 2 1 2 2 2 1 2 2 2
4543 4544 4545 4546 4547 4550 4551 4554 4559 4561 4562 4563 4566
1 2 2 2 2 2 2 1 2 1 2 2 2
4568 4569 4570 4572 4573 4574 4577 4578 4579 4580 4584 4589 4590
1 2 2 2 2 2 1 2 2 1 1 2 2
4592 4594 4595 4596 4597 4598 4599 4600 4601 4602 4605 4606 4608
2 1 2 1 2 2 1 1 2 1 2 2 2
4609 4612 4616 4617 4619 4620 4621 4622 4623 4624 4625 4628 4629
2 1 2 2 2 2 2 2 2 1 1 2 2
4631 4632 4639 4641 4644 4646 4647 4649 4652 4654 4656 4657 4659
1 2 2 2 2 1 1 2 2 2 2 1 2
4660 4664 4666 4667 4670 4671 4673 4674 4675 4676 4677 4678 4680
2 2 2 2 1 2 1 2 1 1 2 1 2
4681 4683 4684 4685 4687 4688 4689 4690 4691 4692 4693 4694 4695
2 2 2 1 1 2 2 2 2 1 1 2 2
4697 4698 4699 4701 4702 4704 4707 4708 4712 4714 4715 4716 4717
2 1 2 1 1 2 2 2 2 1 2 1 1
4722 4724 4729 4730 4734 4735 4737 4739 4740 4742 4743 4745 4746
2 2 2 1 2 2 2 2 2 1 1 2 2
4749 4750 4753 4754 4757 4760 4764 4768 4769 4771 4772 4773 4775
2 2 1 2 2 1 1 1 1 2 1 2 1
4776 4777 4779 4780 4781 4782 4784 4785 4790 4793 4796 4797 4800
2 2 2 2 1 2 2 1 1 2 2 2 2
4802 4804 4806 4807 4813 4814 4816 4817 4819 4820 4821 4824 4826
1 1 1 2 1 1 1 2 1 2 2 2 1
4830 4831 4833 4834 4837 4838 4839 4841 4843 4845 4847 4849 4850
2 2 2 1 2 2 2 2 1 2 2 1 2
4851 4852 4854 4856 4857 4858 4862 4864 4865 4866 4868 4869 4873
2 2 1 2 2 2 2 2 1 1 2 2 2
4874 4878 4879 4882 4886 4887 4889 4892 4894 4896 4897 4899 4900
2 2 2 1 2 2 1 2 1 2 1 2 2
4901 4904 4905 4906 4908 4909 4910 4911 4912 4913 4914 4916 4917
2 1 1 2 2 2 1 2 2 2 2 2 2
4918 4919 4920 4921 4924 4925 4926 4927 4928 4929 4931 4932 4936
2 2 2 1 2 1 1 2 2 2 2 1 1
4939 4940 4943 4944 4950 4951 4952 4953 4955 4957 4959 4961 4962
2 2 2 1 2 1 2 2 2 1 1 1 1
4964 4965 4966 4967 4968 4971 4972 4974 4976 4977 4979 4981 4982
2 1 1 2 2 2 2 2 1 1 2 2 1
4983 4984 4986 4988 4991 4992 4995 4997 4998 4999 5001 5007 5009
1 2 2 2 1 2 2 2 2 2 2 2 2
5010 5011 5015 5016 5017 5018 5021 5024 5025 5026 5027 5028 5030
2 1 2 2 1 2 2 2 2 2 2 2 2
5031 5032 5036 5039 5041 5044 5045 5046 5047 5048 5051 5052 5053
1 2 1 2 2 2 1 2 1 2 2 1 2
5054 5057 5061 5062 5063 5064 5066 5067 5068 5070 5074 5077 5078
2 1 2 2 2 2 2 2 2 2 1 2 2
5079 5081 5083 5084 5085 5086 5087 5088 5090 5091 5094 5096 5097
2 2 2 2 1 1 1 1 1 2 2 2 2
5099 5101 5102 5106 5109 5111 5113 5116 5119 5120 5124 5125 5126
2 2 2 2 1 1 1 1 2 2 2 2 1
5127 5128 5129 5130 5132 5133 5135 5137 5138 5139 5142 5143 5144
1 1 2 2 2 2 2 2 1 1 1 1 2
5145 5148 5150 5152 5153 5154 5155 5156 5157 5159 5164 5165 5166
2 2 1 1 2 2 1 2 1 1 1 2 1
5167 5170 5171 5172 5174 5175 5179 5181 5185 5187 5188 5189 5190
2 2 2 2 2 1 2 1 2 2 1 1 1
5192 5193 5194 5201 5202 5204 5205 5207 5209 5210 5213 5214 5216
2 1 1 2 2 1 2 1 1 2 1 1 2
5218 5223 5224 5227 5233 5235 5236 5239 5240 5243 5245 5247 5248
1 1 1 2 1 2 2 2 2 1 2 2 2
5250 5251 5253 5254 5259 5262 5264 5265 5266 5273 5275 5277 5278
1 1 2 1 2 2 1 2 2 1 2 2 1
5284 5286 5287 5288 5290 5292 5293 5294 5295 5298 5300 5301 5302
2 1 2 2 2 2 1 2 1 2 2 1 1
5303 5305 5306 5307 5311 5313 5315 5316 5320 5321 5322 5325 5326
2 1 2 2 2 1 2 2 2 1 2 2 2
5327 5328 5329 5331 5334 5335 5336 5340 5343 5344 5345 5346 5348
2 1 2 2 1 2 1 2 2 1 1 2 2
5350 5351 5352 5353 5354 5355 5356 5358 5359 5360 5363 5366 5367
2 1 2 1 2 2 2 2 2 1 2 1 2
5368 5369 5371 5373 5375 5376 5377 5379 5381 5382 5383 5387 5389
2 2 2 2 1 1 2 2 2 2 2 2 1
5390 5391 5393 5394 5395 5396 5397 5398 5399 5400 5401 5404 5405
2 2 2 2 1 2 2 2 2 2 2 2 2
5407 5408 5409 5411 5412 5413 5417 5418 5420 5421 5423 5425 5427
1 2 1 2 2 1 1 2 2 2 2 1 1
5428 5429 5430 5431 5433 5434 5437 5438 5439 5441 5442 5443 5444
2 2 2 1 2 2 2 2 2 2 1 2 1
5446 5451 5453 5454 5455 5457 5458 5459 5460 5461 5464 5466 5467
1 2 2 1 2 2 2 2 2 2 2 2 2
5468 5469 5472 5473 5474 5475 5481 5485 5486 5487 5488 5489 5491
1 2 2 1 2 2 1 2 2 1 1 2 2
5493 5494 5495 5498 5499 5506 5509 5510 5511 5514 5516 5517 5518
1 1 1 2 2 1 1 1 2 2 2 2 2
5519 5521 5522 5525 5527 5533 5536 5537 5538 5540 5543 5544 5548
2 1 2 2 1 2 1 2 1 2 2 1 2
5549 5551 5554 5556 5558 5559 5563 5566 5567 5568 5569 5571 5574
1 2 2 2 2 1 2 2 1 2 2 1 2
5575 5576 5577 5578 5580 5581 5583 5584 5587 5589 5590 5591 5592
2 1 1 1 1 1 2 1 1 1 2 1 1
5593 5596 5597 5598 5601 5603 5604 5605 5608 5609 5610 5611 5612
1 1 1 1 1 1 1 1 2 1 1 1 1
5613 5617 5619 5621 5623 5624 5627 5628 5630 5631 5635 5638 5641
1 1 2 2 1 1 1 2 2 2 2 2 2
5643 5645 5646 5647 5652 5655 5656 5657 5658 5660 5662 5663 5664
2 1 2 2 2 1 1 2 2 1 2 2 2
5665 5668 5670 5671 5674 5675 5678 5679 5681 5683 5684 5687 5688
2 2 1 2 2 2 2 2 2 2 2 2 2
5690 5691 5692 5693 5694 5695 5696 5697 5698 5699 5703 5705 5710
2 2 2 2 2 2 2 2 2 2 1 2 1
5711 5715 5716 5717 5718 5720 5721 5723 5726 5727 5728 5729 5730
2 2 2 1 1 2 2 1 2 2 1 1 2
5732 5733 5735 5737 5738 5740 5741 5743 5745 5746 5750 5751 5753
2 1 1 2 1 1 1 1 2 1 1 2 2
5756 5757 5760 5761 5766 5769 5772 5774 5779 5780 5781 5783 5784
1 2 1 2 1 1 1 1 1 1 2 1 1
5788 5789 5790 5792 5793 5795 5796 5799 5803 5807 5809 5812 5813
2 1 1 1 2 1 1 1 1 1 1 1 1
5814 5815 5816 5817 5818 5822 5824 5827 5829 5830 5831 5832 5833
1 1 1 1 1 1 1 1 1 1 1 1 1
5835 5838 5840 5842 5843 5844 5845 5846 5847 5850 5851 5856 5857
1 1 1 1 2 1 1 1 2 1 1 1 1
5858 5859 5863 5864 5866 5869 5870 5871 5872 5874 5875 5878 5879
1 1 1 1 1 1 1 1 2 1 1 1 1
5880 5884 5885 5886 5887 5889 5890 5891 5892 5893 5894 5895 5900
1 1 1 1 1 1 1 1 1 1 1 1 1
5904 5906 5909 5910 5911 5913 5914 5921 5923 5926 5928 5930 5931
1 1 1 1 1 1 1 1 1 1 1 1 1
5932 5934 5936 5937 5938 5940 5943 5944 5946 5949 5950 5951 5952
1 1 1 1 1 1 1 1 1 2 1 1 1
5954 5957 5958 5959 5960 5961 5964 5965 5969 5972 5975 5976 5978
1 1 2 1 1 1 1 2 2 1 1 1 1
5980 5986 5987 5989 5990 5992 5993 5994 5995 5997 6005 6006 6009
2 1 1 1 1 1 1 1 1 1 1 1 1
6010 6013 6014 6015 6017 6019 6020 6021 6022 6025 6027 6029 6030
1 1 1 1 1 1 1 1 1 2 1 1 1
6031 6035 6036 6037 6038 6041 6042 6043 6046 6047 6053 6055 6056
1 1 1 1 1 1 1 1 1 1 1 1 1
6060 6063 6064 6065 6066 6067 6068 6069 6070 6071 6072 6076 6077
1 1 1 1 1 1 1 1 1 1 1 1 1
6078 6079 6082 6084 6085 6086 6087 6088 6090 6093 6098 6099 6100
1 2 1 1 1 1 2 2 1 1 1 1 1
6101 6104 6106 6107 6108 6109 6110 6111 6115 6116 6118 6120 6122
1 1 1 1 1 1 1 1 1 1 1 2 1
6126 6127 6128 6129 6131 6136 6137 6139 6140 6141 6143 6145 6147
1 1 1 1 1 1 1 1 1 1 1 1 1
6148 6149 6150 6151 6152 6154 6155 6158 6160 6163 6165 6166 6167
1 1 1 1 1 1 1 1 1 1 1 1 1
6169 6170 6172 6177 6179 6180 6181 6183 6186 6188 6191 6195 6200
1 1 1 1 1 1 1 1 1 1 1 1 1
6205 6206 6208 6210 6211 6212 6213 6214 6216 6217 6218 6220 6223
1 1 1 1 1 1 1 1 2 1 1 1 1
6224 6225 6226 6229 6230 6231 6232 6234 6235 6236 6237 6238 6239
1 1 1 1 2 2 1 1 2 1 1 1 1
6240 6241 6242 6243 6244 6245 6246 6248 6251 6252 6255 6256 6257
1 1 1 1 1 1 1 1 1 1 2 1 1
6259 6260 6261 6263 6264 6265 6266 6267 6269 6271 6272 6274 6277
1 1 1 1 1 1 1 1 1 1 1 1 1
6278 6279 6280 6282 6283 6284 6287 6290 6292 6293 6294 6295 6296
1 2 1 1 1 1 1 1 1 1 1 1 1
6297 6298 6300 6301 6303 6304 6308 6313 6315 6320 6321 6323 6328
1 1 1 1 1 1 1 1 1 1 1 1 1
6331 6332 6334 6335 6336 6337 6338 6340 6342 6344 6345 6348 6349
1 1 1 1 1 1 1 1 1 1 1 1 1
6351 6352 6354 6357 6358 6359 6362 6364 6365 6366 6369 6372 6373
1 1 1 1 1 1 1 1 1 1 1 2 1
6375 6376 6377 6380 6381 6383 6384 6385 6386 6387 6390 6391 6392
1 1 1 1 1 1 1 1 1 1 1 1 1
6393 6394 6395 6396 6397 6400 6403 6404 6405 6407 6409 6410 6413
1 1 1 1 1 1 1 1 1 1 1 1 1
6414 6416 6417 6419 6422 6425 6428 6429 6430 6431 6432 6433 6434
1 1 1 1 1 2 1 1 1 1 1 1 1
6435 6437 6439 6440 6441 6443 6444 6446 6447 6450 6451 6452 6453
1 1 1 1 1 1 1 1 1 1 1 1 1
6455 6457 6458 6459 6460 6461 6463 6466 6469 6471 6472 6473 6476
1 1 1 1 1 1 1 1 1 1 1 1 1
6477 6479 6480 6481 6482 6483 6484 6486 6487 6488 6491 6493 6494
1 1 1 1 1 1 1 1 1 1 1 1 1
6495 6496 6497 6498 6503 6506 6507 6513 6514 6516 6518 6521 6525
1 1 1 1 1 1 1 1 1 1 1 1 1
6526 6527 6529 6530 6532 6533 6534 6535 6536 6540 6544 6548 6549
1 1 1 1 1 1 1 1 1 1 1 1 1
6550 6551 6553 6555 6556 6557 6562 6563 6564 6565 6567 6569 6573
1 1 1 1 1 1 1 1 1 1 1 1 1
6577 6584 6586 6587 6589 6591 6592 6593 6594 6595 6598 6602 6603
1 1 1 1 1 1 1 1 1 1 1 1 1
6605 6606 6609 6610 6612 6614 6615 6616 6618 6619 6623 6624 6625
1 1 1 1 1 1 1 1 1 1 1 1 1
6626 6627 6630 6632 6634 6635 6638 6640 6641 6642 6644 6645 6647
1 1 1 1 1 2 1 2 1 1 1 1 1
6651 6655 6656 6657 6659 6661 6664 6665 6666 6669 6670 6671 6673
1 2 1 2 2 2 2 1 2 2 2 1 1
6677 6681 6684 6686 6687 6688 6691 6692 6694 6696 6697 6698 6699
2 2 2 1 1 1 1 1 1 2 1 1 1
6700 6702 6706 6707 6708 6710 6711 6712 6713 6714 6715 6716 6717
2 2 1 1 1 1 2 1 1 1 1 1 1
6719 6720 6721 6725 6727 6728 6729 6730 6731 6732 6735 6736 6740
1 2 2 2 2 2 1 1 2 1 2 2 1
6743 6744 6745 6749 6753 6755 6757 6758 6760 6762 6763 6765 6767
1 2 2 1 1 2 1 1 2 2 1 1 1
6769 6770 6771 6772 6773 6780 6781 6783 6785 6786 6787 6789 6790
1 1 2 2 1 2 1 1 2 2 2 1 1
6791 6794 6795 6796 6797 6798 6799 6800 6804 6805 6806 6810 6812
2 2 1 1 2 1 1 2 2 2 1 1 2
6813 6816 6823 6824 6825 6828 6829 6830 6831 6836 6839 6841 6842
1 1 1 1 1 1 1 2 1 1 1 2 2
6845 6847 6852 6853 6857 6858 6860 6861 6862 6866 6867 6868 6869
1 1 1 1 1 1 1 1 2 2 1 1 2
6871 6872 6874 6878 6879 6881 6882 6884 6886 6887 6888 6889 6891
1 2 1 2 1 1 1 1 2 1 2 2 1
6892 6893 6895 6899 6903 6905 6906 6908 6910 6911 6913 6914 6916
1 2 1 1 2 1 1 2 1 2 2 2 1
6920 6922 6923 6927 6928 6929 6930 6931 6935 6936 6938 6939 6941
2 2 2 2 2 1 1 2 1 2 2 2 2
6943 6944 6945 6946 6948 6950 6953 6954 6955 6959 6960 6961 6962
2 2 2 1 2 2 1 2 2 2 1 2 2
6963 6965 6968 6969 6970 6972 6973 6974 6978 6979 6980 6983 6985
2 2 2 1 1 2 1 2 2 2 1 1 2
6986 6987 6991 6992 6997 6998 6999 7003 7004 7005 7006 7007 7009
2 1 1 1 2 1 1 1 1 2 1 1 1
7011 7014 7015 7016 7017 7018 7019 7021 7022 7023 7027 7030 7032
1 1 2 2 2 2 1 2 1 2 1 2 2
7034 7036 7039 7040 7041 7042 7043 7044 7045 7048 7049 7052 7053
1 2 1 2 1 2 2 1 2 1 2 1 2
7054 7055 7059 7061 7062 7064 7065 7067 7070 7071 7072 7075 7076
2 1 2 1 2 1 1 1 2 2 1 1 1
7077 7078 7079 7081 7082 7085 7086 7088 7089 7094 7099 7101 7102
1 1 2 2 1 1 1 1 2 1 2 1 2
7105 7106 7108 7109 7110 7111 7113 7115 7117 7119 7120 7121 7122
1 1 1 2 2 2 2 1 1 1 2 1 1
7124 7125 7126 7128 7129 7131 7135 7137 7138 7142 7143 7144 7147
1 1 1 1 1 1 1 1 2 2 2 1 2
7151 7152 7153 7158 7163 7169 7170 7175 7178 7179 7180 7181 7183
2 2 1 1 2 1 1 2 2 1 2 1 2
7185 7186 7187 7188 7189 7191 7193 7195 7196 7199 7201 7202 7203
1 1 2 1 1 2 2 2 1 1 1 2 1
7208 7209 7211 7212 7215 7217 7219 7222 7226 7227 7229 7230 7233
2 1 1 2 2 1 2 2 2 2 1 2 2
7234 7236 7238 7241 7245 7246 7247 7249 7250 7253 7258 7259 7261
1 1 1 2 2 2 1 1 2 2 1 1 1
7262 7264 7266 7270 7272 7273 7274 7275 7277 7279 7281 7282 7286
1 2 2 1 1 1 1 1 1 1 1 1 1
7287 7296 7300 7304 7306 7307 7310 7311 7312 7315 7316 7317 7319
1 2 2 1 2 1 1 1 1 1 1 1 1
7321 7322 7326 7329 7330 7331 7335 7336 7337 7338 7339 7344 7347
1 1 2 1 2 2 2 2 1 2 2 2 1
7350 7351 7354 7356 7357 7358 7360 7361 7363 7366 7367 7368 7369
2 2 2 2 2 1 2 2 1 1 1 2 2
7370 7371 7373 7374 7375 7376 7377 7383 7385 7386 7387 7389 7390
2 2 1 2 2 1 1 2 1 2 1 1 2
7393 7395 7399 7400 7402 7403 7404 7406 7407 7409 7411 7412 7415
1 1 2 2 1 2 1 2 1 1 2 2 2
7416 7417 7421 7422 7423 7424 7425 7426 7427 7428 7429 7430 7431
2 2 2 2 2 2 2 1 2 1 1 2 2
7432 7434 7438 7442 7443 7446 7448 7449 7450 7453 7454 7455 7459
2 1 2 1 2 2 2 2 2 2 1 1 2
7461 7462 7464 7466 7473 7475 7477 7479 7480 7483 7484 7485 7489
1 2 1 1 2 1 2 1 1 2 2 1 2
7490 7493 7494 7495 7498 7500 7501 7503 7504 7506 7510 7513 7515
1 1 1 2 2 2 2 2 2 2 2 1 1
7518 7519 7522 7523 7524 7527 7528 7530 7531 7532 7534 7536 7544
2 2 2 2 2 1 2 1 2 1 2 1 1
7545 7546 7548 7549 7550 7551 7552 7553 7554 7555 7556 7557 7558
2 2 2 2 2 2 2 2 2 2 2 2 1
7559 7560 7563 7564 7566 7569 7570 7573 7579 7582 7583 7584 7586
1 2 2 1 2 2 2 2 2 2 1 2 2
7588 7589 7591 7595 7601 7606 7608 7609 7610 7613 7615 7620 7621
1 2 1 2 2 2 1 2 1 2 2 1 2
7622 7626 7627 7630 7632 7634 7635 7636 7637 7639 7641 7642 7644
2 2 1 2 1 2 2 2 2 2 1 2 1
7647 7649 7652 7653 7654 7656 7658 7662 7666 7667 7668 7670 7671
2 2 2 2 2 2 2 1 2 2 1 1 2
7672 7673 7675 7678 7683 7684 7685 7686 7688 7690 7691 7694 7700
2 2 2 2 2 2 2 1 2 2 1 1 1
7703 7706 7707 7709 7710 7712 7713 7715 7716 7717 7720 7721 7722
2 2 1 1 1 1 1 1 1 1 1 2 1
7723 7725 7728 7729 7731 7732 7733 7735 7736 7737 7738 7740 7741
2 1 2 2 1 1 1 2 2 1 2 2 1
7746 7747 7750 7751 7752 7757 7760 7761 7762 7766 7772 7773 7775
2 2 2 2 2 1 1 2 2 1 1 1 2
7777 7778 7779 7783 7784 7785 7787 7788 7789 7791 7792 7793 7795
1 1 1 2 2 2 1 1 1 1 1 1 2
7796 7799 7800 7805 7809 7812 7815 7816 7819 7821 7822 7823 7825
1 2 2 1 1 1 2 1 1 1 1 1 1
7829 7838 7839 7840 7841 7842 7843 7844 7846 7847 7848 7849 7850
1 1 1 1 2 1 1 1 1 2 1 1 1
7851 7853 7854 7855 7856 7857 7858 7859 7862 7865 7868 7869 7871
1 1 2 2 2 1 1 2 1 2 1 1 1
7874 7875 7876 7877 7878 7879 7880 7882 7884 7885 7887 7890 7891
1 1 1 1 1 1 1 1 2 2 1 2 1
7892 7893 7898 7899 7900 7901 7902 7904 7907 7908 7909 7910 7912
1 2 2 1 1 1 1 2 2 1 1 1 1
7913 7915 7916 7919 7923 7924 7925 7926 7927 7929 7930 7931 7932
1 1 2 2 1 1 2 1 1 2 1 1 2
7936 7937 7938 7939 7940 7943 7945 7947 7948 7949 7950 7953 7954
1 1 1 1 1 2 2 1 2 2 1 1 2
7955 7957 7963 7964 7966 7967 7969 7973 7974 7975 7976 7981 7985
1 1 1 1 2 1 1 1 2 1 1 1 1
7989 7992 7994 7996 7997 7998 8000 8001 8002 8003 8004 8006 8008
1 1 2 1 1 1 1 1 1 1 1 1 1
8012 8015 8017 8018 8022 8023 8025 8026 8028 8029 8033 8034 8037
1 1 1 1 1 1 1 1 1 1 1 1 1
8038 8042 8044 8045 8048 8049 8052 8054 8055 8056 8057 8058 8059
1 1 1 1 1 1 1 1 1 1 1 1 1
8061 8062 8063 8066 8069 8071 8072 8076 8077 8078 8080 8081 8082
1 1 1 1 1 1 1 1 1 1 1 1 1
8083 8084 8085 8088 8090 8093 8094 8098 8099 8100 8102 8104 8107
1 1 1 1 1 2 1 1 1 1 1 1 1
8109 8111 8112 8113 8114 8116 8117 8118 8123 8124 8126 8127 8129
1 1 1 1 2 1 1 1 1 1 1 1 1
8130 8131 8132 8135 8141 8142 8143 8144 8147 8148 8149 8151 8152
1 1 1 1 1 1 2 1 1 1 1 1 1
8153 8155 8156 8159 8160 8161 8162 8163 8165 8169 8170 8171 8176
1 1 1 1 1 1 1 1 1 1 1 1 1
8177 8179 8181 8183 8184 8186 8187 8189 8190 8194 8196 8197 8199
1 2 1 1 1 1 2 2 1 1 1 1 1
8201 8206 8208 8209 8210 8212 8213 8215 8217 8218 8220 8221 8222
1 1 1 1 1 1 1 1 1 1 1 1 1
8223 8225 8227 8230 8233 8234 8235 8236 8238 8239 8240 8243 8244
1 1 1 1 1 1 1 1 1 1 1 1 1
8245 8246 8248 8249 8251 8252 8253 8256 8258 8259 8260 8263 8264
1 1 1 1 1 1 1 1 1 1 1 1 1
8268 8269 8270 8271 8274 8275 8277 8279 8280 8281 8283 8284 8285
1 1 1 1 1 1 1 1 1 1 1 1 1
8286 8287 8289 8290 8291 8293 8294 8296 8299 8301 8302 8303 8305
1 1 1 1 1 1 1 1 1 1 1 1 1
8306 8308 8309 8312 8314 8315 8318 8319 8320 8321 8322 8323 8329
1 1 1 1 1 2 1 1 1 1 1 1 1
8330 8332 8333 8334 8335 8337 8338 8339 8340 8343 8344 8346 8347
1 1 1 1 1 1 1 1 1 1 1 1 1
8353 8354 8356 8358 8364 8366 8370 8371 8372 8373 8374 8375 8376
1 1 1 1 2 1 1 2 1 1 1 1 1
8377 8378 8379 8380 8381 8382 8384 8385 8389 8390 8394 8395 8396
1 1 1 1 1 1 1 1 1 1 1 1 1
8399 8400 8401 8402 8403 8404 8406 8412 8413 8415 8416 8419 8420
1 1 1 1 1 1 1 1 1 1 1 1 1
8421 8423 8427 8428 8429 8431 8432 8435 8437 8438 8439 8440 8442
1 2 2 1 1 1 1 1 1 1 1 1 1
8445 8446 8447 8449 8451 8452 8458 8459 8461 8462 8463 8465 8469
1 1 1 1 1 1 1 1 1 1 1 1 1
8471 8472 8473 8474 8476 8479 8480 8481 8482 8484 8485 8486 8488
1 1 1 1 1 1 1 1 1 1 1 1 1
8491 8493 8494 8495 8498 8500 8502 8504 8505 8506 8507 8508 8509
1 1 1 1 1 1 1 1 1 1 1 1 1
8511 8512 8514 8515 8516 8517 8519 8527 8528 8529 8530 8533 8535
1 1 1 1 1 2 2 1 1 1 1 1 1
8536 8541 8543 8544 8547 8548 8550 8551 8552 8555 8556 8562 8563
1 1 1 1 1 1 1 1 1 1 2 1 1
8565 8567 8568 8569 8571 8573 8574 8575 8577 8578 8580 8581 8582
1 1 1 1 1 1 1 1 1 1 1 1 1
8583 8584 8585 8587 8591 8593 8596 8598 8600 8602 8603 8604 8605
1 1 1 1 1 1 1 1 1 1 1 1 1
8610 8611 8613 8615 8616 8617 8618 8619 8620 8621 8622 8623 8627
1 1 1 1 1 1 1 1 1 1 1 1 1
8628 8629 8631 8632 8633 8635 8636 8639 8640 8641 8642 8647 8649
2 1 2 1 1 1 1 1 1 1 1 1 1
8651 8652 8654 8655 8658 8659 8660 8661 8662 8663 8665 8666 8667
1 1 1 1 1 1 1 1 1 1 2 1 1
8669 8671 8673 8674 8675 8677 8681 8683 8686 8690 8691 8693 8694
1 1 1 1 1 1 1 1 1 1 1 1 1
8695 8696 8699 8703 8704 8705 8706 8708 8709 8710 8711 8714 8716
1 1 1 1 1 1 1 1 1 1 1 2 1
8717 8718 8719 8724 8726 8728 8731 8735 8737 8744 8745 8747 8748
1 1 1 1 1 1 1 1 1 1 1 1 1
8749 8750 8751 8754 8756 8757 8759 8760 8762 8763 8764 8766 8767
1 1 1 1 2 1 1 1 1 1 1 1 1
8769 8770 8771 8773 8777 8783 8784 8785 8788 8790 8793 8794 8799
1 1 1 1 1 1 1 1 1 1 1 1 1
8801 8802 8803 8804 8807 8809 8810 8811 8814 8815 8817 8818 8820
1 1 2 1 2 1 1 1 1 1 1 1 1
8824 8826 8829 8831 8833 8834 8835 8836 8838 8839 8841 8842 8846
1 1 2 1 1 2 1 1 1 1 1 1 1
8847 8848 8850 8852 8853 8855 8858 8862 8863 8867 8868 8872 8873
1 1 1 1 2 1 1 1 1 1 1 1 1
8876 8880 8881 8884 8885 8888 8892 8893 8894 8895 8899 8901 8902
1 1 1 1 1 1 1 1 1 1 1 1 1
8903 8905 8906 8907 8909 8910 8912 8914 8915 8916 8920 8921 8926
1 1 1 1 1 1 1 1 1 1 1 1 1
8927 8930 8931 8932 8933 8934 8935 8937 8940 8941 8942 8945 8948
1 1 2 1 1 1 1 1 1 1 1 1 1
8954 8955 8957 8958 8960 8963 8964 8966 8967 8968 8972 8973 8974
1 1 1 1 1 1 1 1 1 1 1 1 1
8977 8978 8981 8985 8989 8991 8992 8993 8994 8995 8996 8997 8999
1 1 1 1 2 1 1 1 1 1 1 1 1
9000 9001 9002 9003 9004 9005 9007 9008 9009 9012 9014 9016 9017
1 1 1 1 1 1 1 1 1 1 1 1 2
9018 9020 9021 9022 9024 9027 9028 9030 9031 9032 9035 9036 9037
1 1 1 1 1 1 1 1 2 1 1 1 1
9038 9039 9042 9043 9046 9048 9049 9050 9051 9054 9055 9058 9059
1 1 1 1 1 1 1 1 1 1 1 1 1
9061 9063 9070 9071 9074 9076 9079 9082 9083 9084 9085 9087 9088
1 1 1 1 1 1 1 1 1 1 1 1 1
9091 9092 9094 9096 9098 9102 9103 9105 9107 9108 9109 9113 9114
1 1 1 1 1 1 1 1 1 1 1 1 2
9116 9117 9118 9120 9124 9125 9126 9128 9130 9132 9134 9136 9137
1 1 1 1 1 1 1 1 1 1 1 1 1
9138 9141 9142 9146 9147 9155 9156 9158 9160 9162 9168 9171 9172
1 1 1 1 1 1 1 1 1 1 1 1 1
9173 9174 9175 9176 9177 9178 9179 9180 9182 9184 9186 9187 9188
1 1 1 1 1 1 1 1 1 1 1 1 1
9194 9197 9198 9203 9204 9205 9206 9207 9208 9209 9210 9211 9212
1 1 1 2 1 1 1 1 1 1 1 1 1
9213 9214 9215 9218 9220 9221 9224 9225 9226 9227 9228 9229 9231
1 1 1 1 1 1 2 1 2 1 1 2 2
9232 9235 9239 9241 9242 9244 9245 9247 9248 9249 9250 9252 9253
1 1 1 1 1 1 1 1 1 1 1 1 1
9255 9256 9261 9262 9263 9265 9267 9269 9270 9272 9274 9275 9276
1 1 1 1 1 1 1 1 1 1 1 1 1
9277 9280 9281 9285 9289 9295 9296 9297 9300 9303 9305 9306 9307
1 1 1 1 2 1 1 1 1 1 1 1 1
9308 9309 9313 9314 9315 9316 9317 9318 9319 9320 9322 9326 9328
1 1 1 1 1 1 1 1 1 1 1 2 1
9329 9331 9332 9333 9334 9335 9336 9338 9339 9340 9341 9342 9343
1 1 1 1 1 1 1 1 2 1 1 1 1
9344 9345 9346 9348 9351 9352 9353 9356 9357 9358 9359 9360 9362
2 1 1 1 1 1 1 1 1 1 1 1 1
9364 9365 9367 9368 9369 9370 9372 9373 9374 9375 9378 9379 9381
1 1 1 1 1 1 2 2 1 1 1 1 1
9382 9384 9385 9386 9387 9388 9389 9391 9392 9393 9394 9395 9396
1 1 1 1 1 1 1 1 1 1 1 1 1
9397 9398 9399 9402 9403 9406 9407 9411 9413 9415 9416 9417 9418
1 1 1 1 1 1 1 1 1 1 1 1 1
9419 9421 9422 9423 9424 9425 9426 9428 9429 9430 9431 9432 9436
1 1 1 1 1 1 1 1 1 1 1 1 1
9438 9439 9442 9444 9447 9452 9453 9454 9455 9457 9461 9463 9465
1 1 1 1 1 1 1 1 1 1 1 1 1
9467 9470 9471 9476 9480 9481 9482 9483 9484 9485 9487 9489 9490
1 1 1 1 1 1 1 1 1 1 1 1 1
9491 9495 9498 9499 9500 9502 9503 9505 9506 9507 9508 9509 9511
1 2 1 1 1 1 1 1 1 1 1 1 1
9512 9513 9514 9517 9518 9519 9522 9525 9526 9528 9530 9531 9532
2 1 1 1 1 1 1 1 1 1 1 1 1
9535 9537 9538 9539 9543 9545 9546 9547 9548 9551 9553 9554 9558
1 1 1 1 1 1 1 1 1 1 1 1 1
9561 9562 9563 9564 9568 9572 9573 9575 9576 9577 9578 9579 9582
1 1 1 1 1 1 1 1 1 1 1 1 1
9583 9585 9587 9588 9590 9592 9597 9598 9599 9600 9603 9604 9608
1 1 1 1 1 1 1 1 1 1 1 1 1
9609 9611 9612 9613 9616 9617 9618 9619 9620 9623 9624 9625 9627
1 1 2 1 1 1 1 1 1 1 1 1 1
9628 9631 9634 9635 9636 9638 9640 9641 9643 9648 9650 9652 9653
1 2 1 1 1 1 1 1 1 1 1 1 1
9654 9656 9658 9659 9661 9662 9663 9664 9666 9667 9669 9670 9673
1 1 1 1 1 1 1 1 1 1 1 1 1
9676 9682 9685 9686 9687 9688 9689 9690 9693 9696 9697 9699 9700
1 1 1 1 1 1 1 1 1 1 1 1 1
9701 9705 9708 9710 9711 9712 9713 9714 9715 9717 9718 9719 9720
1 1 1 1 1 1 1 1 1 1 1 1 1
9723 9724 9725 9726 9727 9728 9736 9737 9741 9743 9744 9746 9747
1 1 1 1 1 1 1 1 1 1 1 1 1
9748 9749 9751 9752 9754 9756 9757 9758 9759 9761 9762 9765 9766
1 1 1 1 1 1 1 1 1 1 1 1 1
9767 9768 9769 9771 9774 9776 9777 9778 9780 9783 9786 9787 9789
1 1 1 1 1 1 1 1 1 1 1 1 1
9790 9793 9795 9796 9797 9798 9799 9801 9802 9803 9804 9806 9807
1 1 1 1 1 1 1 1 1 1 1 1 1
9808 9810 9812 9813 9814 9815 9817 9819 9820 9823 9824 9826 9829
1 1 1 1 1 1 1 1 1 1 1 1 1
9832 9834 9835 9837 9840 9842 9843 9845 9846 9849 9850 9852 9855
1 1 1 1 1 1 1 1 1 1 1 1 1
9857 9860 9861 9863 9869 9870 9872 9873 9875 9876 9878 9879 9882
1 1 1 1 1 1 1 1 1 1 1 1 1
9883 9884 9885 9888 9890 9891 9892 9893 9894 9895 9896 9897 9898
1 1 1 1 1 1 1 1 1 1 1 1 1
9899 9902 9903 9904 9906 9907 9911 9912 9913 9914 9916 9917 9918
1 1 1 1 1 1 1 1 1 1 1 1 1
9919 9922 9923 9928 9931 9932 9933 9935 9939 9940 9941 9943 9945
1 1 1 1 1 1 1 1 1 1 1 1 1
9946 9948 9949 9950 9951 9953 9954 9957 9959 9960 9962 9966 9967
1 1 1 1 1 1 1 1 1 1 1 1 1
9968 9973 9974 9975 9977 9981 9982 9983 9984 9985 9988 9989 9990
1 1 1 1 1 1 1 1 1 1 1 1 1
9991 9992 9993 9996 9997 9998 10002 10004 10006 10007 10008 10009 10011
1 1 1 1 1 1 1 1 1 1 1 1 1
10013 10016 10017 10019 10020 10021 10025 10026 10028 10030 10031 10032 10033
1 1 1 1 1 1 1 1 1 1 1 1 1
10034 10036 10040 10041 10047 10048 10051 10052 10058 10059 10060 10061 10062
1 1 1 1 1 1 1 1 1 1 1 1 1
10063 10064 10065 10066 10069 10070 10071 10072 10074 10075 10078 10079 10081
1 1 1 1 1 1 1 1 1 1 1 1 1
10083 10091 10092 10093 10095 10096 10098 10105 10107 10108 10109 10110 10111
1 2 1 1 1 1 1 1 1 1 1 1 1
10113 10115 10117 10119 10120 10121 10122 10123 10124 10125 10126 10127 10129
1 1 1 1 1 1 1 1 1 1 1 1 1
10130 10133 10136 10139 10141 10144 10145 10146 10147 10149 10150 10151 10152
1 1 1 1 1 1 1 1 1 1 1 1 1
10154 10155 10156 10159 10162 10167 10168 10172 10177 10179 10182 10183 10184
1 1 1 1 1 1 1 1 1 1 1 1 1
10186 10187 10188 10189 10190 10192 10201 10202 10209 10211 10213 10220 10221
1 1 1 1 1 1 1 1 1 1 1 1 1
10222 10224 10225 10227 10228 10229 10230 10232 10236 10238 10242 10245 10246
1 1 1 1 1 1 1 1 1 1 1 1 1
10247 10250 10251 10252 10254 10258 10259 10260 10261 10263 10264 10265 10269
1 1 1 2 1 1 1 1 1 1 1 1 1
10270 10271 10274 10276 10278 10280 10282 10283 10286 10287 10288 10290 10291
1 2 1 1 1 1 1 1 1 1 1 1 1
10292 10295 10297 10302 10303 10304 10309 10313 10314 10316 10319 10321 10322
1 1 1 1 1 1 1 1 1 1 1 1 1
10323 10324 10325 10326 10331 10332 10333 10335 10336 10338 10339 10343 10346
1 1 1 1 1 1 1 1 1 1 1 1 1
10347 10351 10352 10353 10354 10357 10359 10362 10367 10368 10370 10372 10374
1 1 1 1 1 1 1 1 1 1 1 1 1
10375 10376 10378 10379 10380 10383 10385 10386 10388 10390 10392 10393 10394
1 1 1 1 1 1 1 1 1 1 1 1 1
10396 10397 10399 10400 10401 10406 10410 10413 10415 10418 10420 10421 10422
1 1 1 1 1 1 1 1 1 1 1 1 1
10427 10438 10440 10441 10445 10446 10447 10448 10452 10453 10454 10456 10457
1 1 1 1 1 1 1 1 1 1 1 1 1
10458 10459 10461 10462 10466 10467 10468 10469 10472 10474 10475 10476 10477
1 1 1 1 1 1 1 1 1 1 1 1 1
10478 10480 10481 10484 10486 10487 10491 10493 10495 10497 10498 10499 10500
1 1 1 1 1 1 1 1 1 1 1 1 1
10501 10504 10506 10507 10508 10510 10512 10514 10519 10523 10527 10528 10529
1 1 1 1 1 1 1 1 1 1 1 1 1
10531 10535 10537 10538 10539 10540 10541 10542 10543 10545 10549 10550 10551
1 1 1 1 1 1 1 1 1 1 1 1 1
10552 10555 10556 10558 10561 10563 10564 10565 10566 10568 10573 10576 10577
1 1 1 1 1 1 1 1 1 1 1 1 1
10578 10579 10581 10583 10586 10587 10588 10589 10591 10594 10595 10597 10598
1 1 1 1 1 1 1 1 1 1 1 1 1
10599 10600 10601 10604 10606 10608 10609 10610 10612 10614 10617 10618 10619
1 1 1 1 1 1 1 1 1 1 1 1 1
10622 10623 10626 10627 10629 10631 10632 10633 10635 10636 10637 10639 10641
1 1 1 1 1 1 1 1 1 1 1 1 1
10642 10643 10644 10645 10646 10648 10649 10650 10651 10653 10654 10655 10656
1 1 1 1 1 1 1 1 1 1 1 1 1
10657 10660 10664 10665 10666 10667 10668 10669 10670 10671 10672 10674 10676
1 1 1 1 1 1 1 1 1 1 1 1 1
10678 10680 10681 10683 10684 10687 10688 10695 10696 10697 10699 10700 10701
1 1 1 1 1 1 1 1 1 1 1 1 1
10702 10704 10706 10708 10714 10715 10716 10717 10719 10720 10722 10723 10725
1 1 1 1 1 1 1 1 1 1 1 1 1
10726 10727 10728 10729 10730 10733 10734 10736 10737 10739 10741 10746 10749
1 1 1 1 1 1 1 1 1 1 1 1 1
10754 10757 10758 10759 10760 10761 10764 10768 10771 10772 10776 10778 10780
1 1 1 1 1 1 1 1 1 1 1 1 1
10783 10786 10790 10791 10794 10795 10796 10800 10801 10802 10803 10806 10807
1 1 1 1 1 1 1 1 1 1 1 1 1
10815 10816 10817 10818 10819 10820 10823 10826 10827 10829 10830 10831 10832
1 1 1 1 1 1 1 1 1 1 1 1 1
10834 10835 10836 10839 10842 10849 10850 10852 10856 10858 10859 10863 10864
1 1 1 1 1 1 1 1 1 1 1 1 1
10865 10866 10867 10870 10872 10875 10876 10877 10879 10880 10883 10884 10888
1 1 1 1 1 1 1 1 1 1 1 1 1
10890 10891 10892 10893 10894
1 1 1 2 1
Within cluster sum of squares by cluster:
[1] 62925.66 51549.89
(between_SS / total_SS = 19.4 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault"
#plot k-mean
fviz_cluster(list(data = data, cluster = km$cluster),
ellipse.type = "norm", geom = "point", stand = FALSE,
palette = "jco", ggtheme = theme_classic())
From the cluster plot, we can observe that the data is primarily divided into two large clusters, one in the first dimension and the other in the second dimension. However, within the first dimension, there are still some distinct groups within Cluster 1 and Cluster 2.
While the existence of two distinct clusters in the first dimension is not necessarily a bad thing, as it may suggest that the data within these clusters shares certain characteristics or features. On the other hand, the three clusters in the second dimension could be considered an indication of sub-populations or groups within the subset.
Overall, the clustering outcome in this plot can provide insights into the distribution of the data and help identify patterns or relationships that may not be apparent otherwise. However, further analysis and validation would be required to determine the effectiveness and usefulness of these clusters.
#avg silhouette
library(cluster)
sil <- silhouette(km$cluster, dist(data))
rownames(sil) <- rownames(data)
fviz_silhouette(sil)
cluster size ave.sil.width 1 1 3435 0.21 2 2 2459 0.15
#Total within-cluster-sum of square
km$tot.withinss
cluster_assignments <- c(km$cluster)
ground_truth_labels <- c(dataset$satisfaction)
data <- data.frame(cluster = cluster_assignments, label = ground_truth_labels)
# Function to calculate BCubed precision and recall
calculate_bcubed_metrics <- function(data) {
n <- nrow(data)
precision_sum <- 0
recall_sum <- 0
for (i in 1:n) {
cluster <- data$cluster[i]
label <- data$label[i]
# Count the number of items from the same category within the same cluster
same_category_same_cluster <- sum(data$label[data$cluster == cluster] == label)
# Count the total number of items in the same cluster
total_same_cluster <- sum(data$cluster == cluster)
# Count the total number of items with the same category
total_same_category <- sum(data$label == label)
# Calculate precision and recall for the current item and add them to the sums
precision_sum <- precision_sum + same_category_same_cluster /total_same_cluster
recall_sum <- recall_sum + same_category_same_cluster / total_same_category
}
# Calculate average precision and recall
precision <- precision_sum / n
recall <- recall_sum / n
return(list(precision = precision, recall = recall))
}
# Calculate BCubed precision and recall
metrics <- calculate_bcubed_metrics(data)
# Extract precision and recall from the metrics
precision <- metrics$precision
recall <- metrics$recall
# Print the results
cat("BCubed Precision:", precision, "\n")
cat("BCubed Recall:", recall, "\n")
BCubed Precision: 0.6249162 BCubed Recall: 0.6351828
6.2.2 - cluster k=3 :¶
#calculate k-mean
km <- kmeans(data1, 3, iter.max = 140 , algorithm="Lloyd", nstart=100)
km
K-means clustering with 3 clusters of sizes 1732, 2107, 2055
Cluster means:
Gender Customer.Type Age Type.of.Travel Class Seat.comfort
1 1.500000 1.236721 0.4395742 1.713626 2.058314 1.883949
2 1.459896 1.103939 0.4560532 1.674419 2.144281 4.000475
3 1.562044 1.269100 0.4228311 1.690024 1.780049 2.382968
Departure.Arrival.time.convenient Food.and.drink Inflight.wifi.service
1 1.789838 1.653002 3.831986
2 3.939250 3.910774 3.740389
3 2.985888 2.723114 2.129927
Inflight.entertainment Online.support Ease.of.Online.booking On.board.service
1 3.239030 4.118938 4.193418 3.688222
2 4.135263 4.136687 4.145705 3.838159
3 2.556204 2.294404 2.061314 2.764964
Leg.room.service Baggage.handling Checkin.service Cleanliness Online.boarding
1 3.678984 3.841224 3.463048 3.886836 4.034065
2 3.811580 4.005221 3.622212 3.981016 3.940674
3 2.897324 3.167397 2.802920 3.187835 2.068127
Arrival.Delay.in.Minutes
1 0.02305028
2 0.02141366
3 0.02597246
Clustering vector:
1 2 3 4 6 7 8 10 11 12 13 14 18
3 1 1 3 1 1 3 3 1 3 1 1 3
19 20 21 22 24 25 28 29 31 32 33 34 36
3 1 1 1 1 3 3 1 3 3 1 1 1
37 39 42 45 46 47 49 50 53 54 55 56 60
3 1 1 1 3 3 3 3 3 1 1 3 3
62 63 69 70 71 73 74 75 76 81 82 83 85
3 1 3 3 3 1 3 1 3 3 3 3 3
88 89 90 92 93 94 96 97 98 100 101 104 105
3 1 1 3 3 3 1 1 1 1 1 3 1
106 107 108 109 112 113 114 115 122 125 126 127 128
1 3 1 3 1 3 1 1 3 3 3 3 1
129 130 134 135 136 139 142 147 148 149 151 152 155
1 1 1 3 1 1 3 1 3 3 1 1 1
160 161 162 163 165 168 169 170 171 173 175 176 177
3 3 1 1 1 1 3 3 3 3 3 1 3
178 183 184 185 186 188 189 196 197 199 203 205 209
3 3 1 1 1 2 3 1 3 1 3 3 1
211 212 213 217 221 222 223 226 227 232 234 238 239
3 3 1 3 3 1 3 3 1 3 1 3 1
241 242 245 247 249 251 252 254 255 257 259 261 262
3 3 1 3 3 3 3 3 3 1 1 3 3
264 267 271 273 274 275 276 278 281 283 285 286 287
1 3 1 1 3 3 3 2 3 3 1 3 3
288 289 290 291 292 293 295 297 299 301 303 307 308
1 3 1 3 1 1 3 3 3 1 1 3 1
310 311 317 319 320 322 324 325 326 328 330 332 334
1 3 3 3 3 1 3 3 3 3 3 3 1
335 337 338 341 342 346 348 349 351 354 356 358 359
1 1 1 3 3 3 3 3 1 3 3 3 3
361 362 363 364 366 369 372 373 375 376 379 380 381
1 1 1 3 1 1 1 1 3 3 1 3 3
382 384 386 387 388 390 391 394 398 400 401 402 406
3 3 3 1 3 1 3 3 1 3 3 3 1
407 408 409 411 413 414 416 421 423 430 431 432 433
3 3 1 1 1 1 1 1 3 3 1 3 3
435 436 438 440 442 444 445 448 451 453 455 456 459
1 3 3 1 1 1 3 3 3 1 1 2 1
462 463 464 465 466 467 468 475 476 477 480 481 483
1 3 3 3 2 3 3 3 1 3 1 3 1
484 487 490 492 495 496 498 499 500 503 504 505 508
3 1 3 1 3 3 3 1 3 1 3 1 3
511 515 516 519 521 524 526 527 530 532 533 536 537
3 3 3 1 1 3 1 3 3 3 1 1 1
538 539 543 544 545 547 551 552 553 554 556 558 559
3 3 1 3 3 1 3 3 3 1 3 1 3
560 562 564 565 569 571 572 573 575 579 581 586 587
1 1 1 3 3 3 1 1 2 2 2 3 3
589 592 593 595 596 597 602 604 605 607 609 614 615
1 3 3 3 3 1 3 1 3 1 1 3 3
617 619 620 622 626 631 635 636 638 640 641 643 646
1 3 1 3 3 3 1 3 3 3 3 3 3
647 648 649 651 653 655 656 657 658 659 660 662 663
3 1 1 3 3 1 3 3 2 1 1 3 3
664 666 667 669 670 673 675 677 679 684 686 687 688
3 3 3 1 1 3 3 3 1 3 3 3 3
691 692 693 694 696 701 706 707 708 711 712 715 717
3 3 3 3 3 3 3 1 3 3 3 3 1
718 720 721 722 724 725 727 728 729 730 734 735 736
1 3 3 3 1 3 1 1 2 2 2 2 3
737 738 739 740 741 742 743 744 745 746 747 748 749
3 2 2 3 1 1 1 1 1 1 3 1 3
751 752 753 756 757 758 759 761 762 764 765 766 767
1 3 3 1 3 1 3 3 3 3 3 1 3
768 769 771 776 777 779 781 782 784 787 788 794 795
3 1 1 1 3 1 3 1 3 3 3 1 3
796 802 803 804 806 809 814 815 818 819 821 822 823
1 1 1 1 3 1 1 3 3 1 1 3 3
825 826 828 830 831 832 833 835 836 842 843 847 848
3 1 3 3 3 3 1 3 3 3 3 3 3
850 851 853 858 859 860 864 867 869 870 872 874 877
3 3 1 3 3 3 3 3 3 3 3 1 3
880 881 883 886 887 888 889 892 893 894 897 903 904
3 3 3 3 1 3 1 3 3 1 1 3 3
905 906 907 908 909 912 913 914 916 917 919 922 923
3 2 2 2 2 2 2 2 2 3 3 3 2
924 925 927 929 931 932 933 938 940 941 942 944 945
3 3 3 3 2 2 2 2 2 2 2 2 3
947 949 952 954 955 956 958 961 963 964 969 970 972
2 3 2 2 3 3 2 2 3 3 2 3 2
973 974 977 978 979 980 982 983 985 986 987 988 990
2 3 2 2 2 2 3 3 3 3 2 3 3
992 993 994 997 1000 1005 1007 1011 1015 1016 1018 1019 1021
3 3 2 2 3 2 2 2 2 2 3 2 2
1022 1023 1024 1025 1027 1028 1031 1032 1036 1037 1038 1039 1043
2 2 3 2 2 3 3 3 2 2 3 1 3
1048 1049 1050 1051 1052 1053 1054 1055 1057 1058 1061 1062 1063
3 2 3 3 2 2 2 3 2 2 2 3 2
1065 1066 1070 1071 1072 1076 1079 1080 1081 1082 1088 1091 1093
3 2 3 3 2 2 3 2 2 3 2 3 3
1095 1096 1098 1099 1101 1104 1109 1110 1112 1114 1115 1116 1118
2 3 2 2 3 3 2 2 2 3 2 2 2
1119 1120 1121 1122 1123 1125 1127 1133 1134 1135 1136 1137 1138
2 2 2 3 3 3 2 3 2 2 2 2 3
1140 1141 1142 1145 1146 1148 1151 1153 1155 1156 1158 1159 1160
3 3 2 2 2 3 3 2 3 2 2 2 2
1161 1162 1163 1164 1166 1168 1169 1170 1173 1174 1181 1182 1189
3 3 3 2 2 3 2 2 2 2 2 2 3
1191 1192 1193 1194 1201 1202 1203 1204 1210 1212 1213 1214 1221
2 3 3 2 2 2 2 2 1 1 3 1 3
1223 1224 1225 1226 1227 1228 1229 1230 1231 1236 1238 1240 1242
3 1 3 2 3 3 1 2 3 2 3 2 2
1243 1244 1246 1247 1249 1250 1251 1252 1253 1254 1256 1257 1259
2 3 2 2 3 3 3 3 3 3 3 2 3
1261 1262 1263 1264 1267 1268 1270 1271 1272 1273 1274 1275 1276
2 3 3 3 2 3 3 3 2 3 3 2 2
1277 1280 1281 1282 1283 1285 1287 1288 1289 1290 1291 1292 1293
2 2 2 2 3 3 3 2 3 3 2 3 2
1295 1297 1298 1301 1304 1307 1313 1316 1317 1319 1320 1324 1325
3 3 2 2 2 3 2 3 3 2 3 3 1
1326 1330 1334 1340 1342 1344 1347 1349 1350 1353 1354 1356 1358
3 2 2 3 2 2 3 3 2 2 3 2 2
1359 1360 1361 1362 1364 1365 1369 1370 1373 1374 1376 1378 1383
3 2 2 3 2 2 2 2 2 2 3 2 2
1385 1386 1387 1388 1393 1394 1397 1400 1403 1406 1408 1409 1412
2 2 2 3 3 2 3 2 2 2 2 3 3
1417 1418 1420 1421 1422 1424 1425 1429 1430 1431 1433 1434 1436
2 2 3 2 3 2 2 2 2 3 3 3 2
1437 1439 1440 1443 1444 1445 1447 1449 1451 1453 1460 1461 1463
2 1 1 3 3 3 2 3 2 3 2 2 2
1464 1466 1467 1468 1470 1471 1472 1473 1474 1476 1478 1479 1483
3 2 3 2 2 2 2 3 2 3 3 2 2
1486 1490 1491 1492 1495 1496 1497 1498 1500 1503 1504 1505 1507
2 3 2 2 2 2 3 3 2 2 2 3 2
1509 1510 1511 1514 1516 1517 1518 1519 1526 1528 1531 1533 1537
2 2 2 3 2 2 2 3 2 2 3 2 3
1538 1539 1541 1545 1547 1548 1549 1554 1555 1556 1561 1564 1566
2 2 2 3 2 2 2 1 1 1 3 1 1
1567 1569 1570 1571 1572 1574 1575 1576 1577 1578 1579 1580 1582
1 1 3 1 1 3 3 3 3 3 3 3 1
1587 1588 1589 1590 1592 1593 1594 1595 1596 1598 1599 1603 1604
1 3 3 1 1 1 1 1 1 1 1 3 3
1606 1607 1609 1610 1611 1612 1614 1616 1618 1619 1623 1624 1625
3 3 3 3 3 3 3 1 1 1 3 1 3
1626 1629 1630 1632 1634 1636 1637 1638 1639 1641 1642 1643 1646
3 3 3 3 1 3 3 3 3 1 3 1 3
1647 1648 1649 1650 1656 1659 1660 1661 1663 1665 1667 1669 1670
3 1 1 1 1 1 3 3 3 1 3 3 1
1671 1672 1673 1676 1677 1679 1681 1682 1683 1689 1690 1692 1693
1 1 1 1 1 3 1 1 1 3 1 3 3
1694 1696 1698 1699 1700 1707 1708 1709 1712 1714 1718 1719 1722
3 1 3 1 1 1 3 1 1 3 3 1 1
1723 1724 1729 1732 1733 1734 1735 1736 1737 1740 1741 1742 1744
1 1 3 1 1 1 1 1 1 1 1 1 3
1747 1748 1749 1752 1753 1754 1755 1756 1758 1759 1760 1763 1767
3 3 3 1 1 1 1 3 1 1 3 3 1
1768 1770 1771 1772 1775 1777 1778 1781 1782 1783 1785 1786 1795
1 1 3 3 3 1 1 1 1 3 3 1 3
1797 1798 1799 1800 1801 1802 1805 1806 1807 1809 1810 1812 1813
3 1 3 3 3 3 3 1 1 3 3 1 1
1814 1816 1817 1821 1823 1824 1825 1827 1828 1830 1831 1833 1835
1 3 1 1 3 3 3 1 1 3 3 3 3
1837 1838 1841 1842 1846 1847 1848 1849 1850 1851 1853 1854 1855
3 1 3 3 1 1 1 1 1 3 3 1 3
1858 1860 1862 1868 1869 1871 1874 1875 1876 1878 1879 1881 1882
1 1 1 3 1 3 3 1 1 1 1 3 3
1883 1884 1889 1890 1892 1893 1895 1896 1899 1901 1903 1904 1905
1 1 1 1 1 1 3 3 1 1 3 1 1
1906 1907 1910 1913 1914 1917 1919 1921 1926 1928 1929 1931 1933
1 3 1 3 1 1 1 3 1 1 3 1 3
1934 1935 1936 1939 1941 1942 1943 1944 1945 1947 1948 1950 1955
1 3 3 3 3 3 3 1 1 1 1 1 3
1959 1960 1961 1962 1964 1966 1967 1971 1972 1973 1974 1975 1976
3 3 1 1 1 3 1 1 3 1 1 1 1
1978 1979 1981 1982 1984 1986 1988 1989 1990 1994 1996 1997 1999
1 1 3 1 3 3 1 3 1 1 1 3 1
2000 2001 2002 2003 2006 2007 2009 2011 2015 2019 2020 2021 2022
3 1 1 1 1 3 1 3 3 3 3 1 3
2023 2026 2027 2029 2031 2032 2034 2035 2036 2038 2041 2042 2043
1 1 3 3 3 1 1 3 1 3 3 3 3
2045 2046 2047 2048 2050 2051 2053 2054 2055 2057 2059 2060 2061
1 3 1 1 3 1 1 3 3 3 3 1 3
2062 2063 2065 2066 2068 2069 2070 2071 2072 2073 2074 2076 2077
1 1 3 1 1 3 1 3 1 3 1 1 1
2080 2083 2084 2085 2086 2088 2089 2090 2092 2094 2095 2096 2098
1 1 3 1 3 1 3 1 3 1 1 3 1
2100 2105 2110 2112 2115 2117 2119 2121 2123 2124 2126 2129 2132
3 1 1 3 3 3 3 3 3 1 3 1 3
2133 2135 2136 2137 2138 2140 2141 2142 2145 2147 2148 2149 2150
3 1 3 1 1 1 1 3 1 1 1 1 1
2151 2152 2153 2154 2155 2156 2157 2161 2163 2166 2168 2173 2177
1 1 3 1 3 3 3 3 3 1 3 1 1
2181 2182 2184 2185 2188 2189 2190 2191 2192 2195 2196 2198 2201
3 1 3 3 1 3 3 1 1 1 1 1 1
2202 2209 2210 2211 2212 2215 2216 2220 2225 2227 2229 2230 2231
3 3 3 1 3 1 1 3 1 3 3 3 3
2232 2235 2237 2241 2243 2244 2245 2246 2247 2249 2250 2252 2254
3 1 1 1 3 3 1 3 1 1 1 3 3
2255 2256 2258 2259 2260 2261 2265 2269 2271 2274 2275 2276 2279
3 1 3 3 1 3 3 3 1 3 1 3 1
2280 2281 2282 2284 2285 2288 2289 2290 2297 2299 2301 2303 2304
1 3 1 2 1 3 3 3 1 3 3 1 3
2305 2307 2309 2310 2311 2312 2314 2316 2317 2318 2320 2321 2322
1 1 3 3 1 1 3 3 3 3 3 3 1
2323 2328 2331 2333 2334 2335 2337 2341 2342 2344 2345 2346 2347
3 3 1 1 1 1 3 3 1 3 1 3 1
2348 2349 2351 2352 2354 2355 2356 2357 2358 2359 2363 2364 2365
1 3 1 1 3 3 1 1 1 1 3 1 1
2366 2368 2369 2370 2373 2375 2376 2377 2378 2382 2383 2388 2389
1 1 1 3 3 3 1 1 3 1 1 1 1
2390 2393 2394 2395 2397 2398 2401 2403 2404 2407 2408 2409 2411
3 1 3 1 3 3 3 3 3 1 1 1 1
2413 2414 2415 2417 2419 2421 2422 2424 2425 2426 2427 2429 2430
3 3 3 1 3 3 1 1 3 3 1 3 3
2431 2432 2433 2440 2441 2442 2443 2446 2447 2450 2451 2454 2456
1 3 1 1 1 3 3 2 3 3 3 3 1
2457 2458 2460 2464 2465 2467 2470 2473 2474 2476 2479 2480 2481
2 2 2 2 2 1 1 3 1 3 3 3 3
2482 2484 2490 2493 2494 2495 2502 2505 2506 2509 2510 2511 2513
3 1 2 1 3 2 2 2 3 1 3 3 2
2514 2516 2518 2519 2520 2524 2532 2536 2537 2540 2541 2542 2543
3 3 3 2 3 3 3 3 1 3 3 1 3
2544 2546 2547 2548 2553 2554 2558 2559 2560 2561 2562 2566 2567
1 3 3 1 3 2 3 2 1 2 1 1 3
2568 2569 2571 2572 2575 2576 2577 2578 2580 2582 2587 2588 2589
3 3 3 3 3 3 1 3 3 3 2 2 3
2590 2591 2592 2596 2598 2599 2600 2603 2607 2608 2611 2614 2618
3 2 3 2 3 3 3 3 2 1 1 3 3
2619 2620 2623 2625 2626 2627 2628 2629 2631 2633 2636 2638 2639
1 3 1 3 2 3 1 3 3 2 3 2 3
2640 2642 2643 2646 2649 2650 2651 2652 2653 2654 2655 2656 2662
1 3 1 3 2 3 3 3 3 3 3 3 3
2668 2670 2671 2672 2673 2675 2677 2680 2682 2683 2686 2688 2691
3 3 3 3 3 1 3 1 1 3 1 3 1
2692 2693 2694 2696 2697 2698 2699 2700 2704 2705 2708 2709 2710
3 2 1 1 2 3 2 3 2 1 3 3 1
2711 2712 2713 2715 2716 2717 2720 2722 2723 2725 2726 2727 2737
3 3 1 2 3 3 2 3 2 3 3 2 3
2740 2741 2742 2747 2748 2749 2750 2753 2757 2758 2760 2763 2764
3 3 3 3 3 3 2 3 3 3 3 2 3
2765 2767 2768 2769 2771 2777 2779 2786 2788 2789 2790 2791 2792
2 2 3 3 2 2 3 3 2 3 3 1 1
2794 2795 2796 2797 2799 2800 2801 2803 2807 2808 2810 2812 2817
3 1 1 1 1 1 1 1 2 3 2 2 2
2818 2820 2822 2824 2825 2826 2827 2829 2830 2831 2835 2836 2837
1 2 3 3 2 3 2 2 3 2 2 2 2
2839 2841 2842 2843 2844 2846 2847 2848 2849 2852 2854 2855 2856
3 3 3 2 2 3 3 2 3 3 2 3 2
2858 2860 2863 2865 2866 2867 2874 2877 2878 2880 2882 2887 2888
2 3 3 2 3 2 2 3 2 3 2 3 2
2890 2892 2894 2895 2898 2900 2901 2905 2907 2909 2910 2911 2915
2 2 2 3 2 3 2 2 3 3 3 3 3
2917 2920 2922 2924 2925 2926 2930 2932 2935 2936 2942 2943 2944
2 2 2 2 3 3 3 2 3 2 2 2 2
2946 2947 2950 2952 2953 2954 2957 2958 2960 2962 2964 2965 2969
2 3 2 2 2 2 2 2 2 2 3 2 2
2975 2979 2980 2981 2983 2985 2986 2988 2990 2994 2996 2997 2999
1 3 3 3 3 3 3 3 3 3 3 3 3
3003 3006 3008 3010 3012 3013 3018 3023 3024 3025 3026 3027 3028
3 3 3 3 3 3 3 3 3 3 3 3 3
3030 3033 3034 3036 3038 3039 3043 3044 3045 3047 3049 3051 3052
3 3 3 3 3 3 3 3 3 3 3 3 3
3053 3055 3056 3058 3059 3062 3064 3065 3068 3069 3070 3071 3072
3 3 3 3 3 3 3 3 3 3 3 3 3
3073 3075 3077 3078 3083 3086 3093 3095 3097 3098 3099 3101 3103
3 3 3 3 3 3 3 3 3 3 3 3 3
3110 3111 3112 3113 3114 3115 3116 3118 3119 3121 3122 3123 3125
3 3 3 3 3 3 3 3 3 3 3 3 3
3127 3128 3130 3132 3133 3134 3136 3137 3138 3139 3140 3142 3143
3 3 3 3 3 3 3 3 3 3 3 3 3
3145 3146 3150 3152 3155 3159 3161 3167 3169 3178 3179 3181 3182
3 3 3 3 3 3 3 3 3 3 3 3 3
3184 3185 3187 3189 3190 3193 3194 3196 3197 3198 3200 3201 3202
3 3 3 3 3 3 3 3 3 3 3 3 3
3204 3205 3209 3211 3212 3214 3216 3217 3220 3222 3223 3224 3226
3 3 3 3 3 3 3 3 3 3 3 3 3
3229 3230 3231 3233 3236 3237 3239 3247 3248 3249 3250 3252 3255
3 3 3 3 3 3 3 3 3 3 3 3 3
3256 3260 3262 3265 3266 3267 3269 3271 3272 3273 3274 3276 3282
3 3 3 3 3 3 3 3 3 3 3 3 3
3285 3287 3291 3294 3297 3298 3299 3300 3301 3302 3303 3305 3309
3 3 3 3 3 3 3 3 3 3 3 3 3
3311 3315 3317 3318 3321 3322 3323 3326 3327 3328 3329 3331 3334
3 3 3 3 3 3 3 3 3 3 3 3 3
3337 3338 3341 3343 3347 3348 3350 3355 3357 3358 3364 3370 3372
3 3 3 3 3 3 3 3 3 3 3 3 3
3373 3374 3376 3380 3381 3382 3383 3389 3391 3394 3395 3396 3403
3 3 3 3 3 3 3 3 3 3 3 3 3
3407 3408 3409 3411 3413 3414 3415 3418 3419 3420 3422 3423 3427
3 3 3 3 3 3 3 3 3 3 3 3 3
3431 3432 3433 3434 3438 3441 3444 3448 3450 3452 3453 3455 3456
3 3 3 3 3 3 3 3 3 3 3 3 3
3457 3458 3459 3462 3464 3465 3467 3469 3470 3473 3474 3476 3477
3 3 3 3 3 3 3 3 3 3 3 3 3
3479 3480 3481 3482 3483 3484 3487 3490 3494 3495 3497 3498 3499
3 3 3 3 3 3 3 3 3 3 3 3 3
3500 3502 3503 3504 3505 3510 3511 3512 3515 3516 3517 3518 3521
3 3 3 3 3 3 3 3 3 3 3 3 3
3523 3524 3525 3528 3529 3531 3532 3537 3538 3540 3543 3545 3546
3 3 3 3 3 3 3 3 3 3 3 3 3
3548 3549 3551 3552 3555 3556 3557 3558 3560 3563 3564 3566 3567
3 3 3 1 3 3 3 3 3 3 3 3 3
3568 3570 3571 3573 3575 3576 3577 3578 3579 3580 3584 3585 3587
3 3 3 3 3 3 3 3 3 3 3 3 3
3588 3589 3590 3591 3601 3604 3605 3607 3608 3609 3610 3611 3612
3 3 3 3 3 3 3 3 3 3 3 3 3
3614 3616 3617 3618 3624 3625 3627 3628 3631 3634 3635 3637 3640
3 3 3 3 3 3 3 3 3 3 3 3 3
3641 3643 3644 3645 3646 3647 3648 3653 3654 3655 3658 3661 3663
3 2 3 3 3 3 3 3 3 3 3 3 3
3664 3666 3668 3669 3671 3672 3673 3675 3677 3679 3680 3684 3685
3 3 2 3 3 3 3 3 3 3 3 1 3
3686 3692 3693 3695 3696 3698 3699 3700 3701 3702 3703 3707 3709
3 1 3 3 3 3 3 3 1 3 3 3 3
3710 3712 3713 3714 3715 3716 3718 3719 3720 3721 3726 3731 3735
3 3 3 3 3 3 3 3 3 3 3 2 3
3737 3739 3741 3742 3745 3746 3748 3752 3753 3754 3755 3756 3757
3 3 3 3 3 3 3 3 3 3 3 3 3
3760 3761 3762 3764 3767 3771 3772 3781 3784 3787 3788 3790 3791
3 3 3 3 3 3 3 3 3 3 3 3 3
3793 3795 3796 3797 3801 3802 3804 3805 3807 3810 3811 3815 3817
3 3 3 2 1 3 3 3 3 3 3 3 3
3818 3822 3823 3825 3826 3830 3831 3832 3834 3835 3836 3838 3839
3 3 3 3 3 3 3 3 3 3 2 2 1
3840 3845 3847 3848 3849 3850 3851 3852 3853 3854 3857 3858 3859
3 3 3 3 3 3 3 2 3 3 1 3 3
3860 3861 3863 3867 3868 3873 3875 3876 3877 3881 3882 3883 3885
3 2 3 3 3 3 3 3 2 3 1 3 3
3886 3887 3888 3890 3891 3892 3893 3896 3898 3901 3904 3905 3907
1 2 3 3 1 2 1 3 2 3 3 3 3
3908 3913 3915 3916 3921 3923 3924 3926 3928 3931 3932 3934 3935
2 3 3 3 1 3 1 3 3 3 3 3 3
3941 3942 3943 3944 3952 3953 3954 3957 3958 3959 3963 3964 3965
2 3 3 3 3 2 3 3 3 3 3 3 3
3967 3970 3973 3974 3975 3977 3978 3979 3982 3983 3985 3986 3987
3 1 3 2 1 3 3 3 3 3 2 3 2
3988 3989 3994 3996 3998 3999 4000 4001 4004 4005 4007 4010 4013
3 2 3 2 3 3 2 3 3 3 3 3 3
4016 4017 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029
2 3 3 3 3 3 3 3 3 3 3 3 2
4031 4034 4037 4039 4041 4043 4044 4047 4049 4050 4051 4053 4058
3 3 3 3 3 1 1 2 3 2 3 3 3
4063 4064 4066 4071 4073 4074 4076 4077 4078 4080 4082 4086 4087
3 3 3 3 3 3 2 3 1 3 3 1 1
4088 4089 4090 4091 4092 4093 4094 4095 4098 4099 4104 4105 4106
3 3 3 2 3 3 3 3 3 1 2 3 3
4107 4108 4109 4111 4112 4114 4116 4119 4120 4121 4124 4125 4128
2 3 1 2 3 3 3 3 3 3 3 3 3
4129 4131 4132 4134 4137 4139 4141 4144 4145 4146 4147 4148 4151
3 2 3 3 3 3 3 3 3 3 2 3 3
4152 4154 4155 4156 4157 4162 4166 4167 4169 4170 4171 4172 4173
2 3 3 3 1 2 1 3 1 1 3 3 3
4175 4177 4178 4180 4181 4184 4185 4186 4190 4194 4197 4198 4200
1 3 3 2 2 1 1 1 2 1 2 1 3
4204 4205 4207 4210 4212 4213 4216 4217 4220 4221 4223 4226 4230
1 1 3 1 2 2 1 2 1 1 2 2 3
4231 4240 4241 4242 4244 4245 4249 4250 4251 4254 4255 4256 4257
3 1 2 2 1 1 2 1 1 2 2 3 1
4259 4260 4261 4269 4271 4272 4274 4276 4277 4278 4280 4282 4283
2 2 2 1 1 2 2 2 1 2 1 2 2
4287 4288 4289 4290 4291 4293 4294 4295 4297 4298 4301 4303 4305
1 2 3 2 1 2 2 3 2 2 2 1 3
4306 4308 4309 4310 4313 4314 4315 4316 4317 4318 4320 4321 4323
2 2 2 2 2 1 2 1 3 1 1 2 2
4325 4326 4328 4329 4330 4332 4333 4336 4337 4339 4341 4342 4343
2 1 3 2 1 1 2 2 1 2 1 2 2
4344 4347 4352 4353 4356 4357 4358 4359 4361 4362 4363 4367 4368
1 1 1 2 1 1 2 1 2 1 2 2 1
4369 4370 4371 4372 4375 4376 4378 4379 4380 4382 4383 4384 4386
1 3 1 1 1 1 1 2 2 2 2 2 2
4387 4388 4390 4391 4392 4394 4395 4400 4401 4404 4405 4406 4409
2 2 2 2 2 2 2 2 2 2 2 2 2
4411 4413 4415 4416 4422 4423 4427 4430 4431 4432 4434 4435 4438
2 1 3 3 1 1 3 3 3 3 2 2 3
4439 4441 4444 4446 4447 4449 4450 4451 4452 4454 4455 4456 4459
1 3 1 3 1 3 3 3 2 2 3 1 3
4460 4461 4463 4468 4474 4475 4476 4479 4481 4484 4485 4488 4489
3 2 3 1 3 3 3 3 1 3 2 3 1
4491 4493 4494 4499 4500 4501 4502 4503 4504 4506 4507 4509 4510
3 3 3 1 3 2 1 3 3 2 3 1 3
4511 4514 4516 4517 4518 4528 4531 4532 4533 4537 4538 4540 4542
1 2 3 3 3 1 3 3 3 2 1 3 3
4543 4544 4545 4546 4547 4550 4551 4554 4559 4561 4562 4563 4566
2 3 3 3 3 3 3 1 3 2 3 1 3
4568 4569 4570 4572 4573 4574 4577 4578 4579 4580 4584 4589 4590
2 3 3 3 3 1 2 1 1 1 1 1 3
4592 4594 4595 4596 4597 4598 4599 4600 4601 4602 4605 4606 4608
3 2 3 2 3 3 2 1 3 1 3 3 3
4609 4612 4616 4617 4619 4620 4621 4622 4623 4624 4625 4628 4629
3 2 3 3 3 3 3 3 3 2 1 3 3
4631 4632 4639 4641 4644 4646 4647 4649 4652 4654 4656 4657 4659
2 3 3 1 3 2 1 3 3 1 3 2 3
4660 4664 4666 4667 4670 4671 4673 4674 4675 4676 4677 4678 4680
3 3 3 3 1 3 1 2 2 2 3 1 3
4681 4683 4684 4685 4687 4688 4689 4690 4691 4692 4693 4694 4695
3 3 3 1 2 3 3 3 3 1 1 3 3
4697 4698 4699 4701 4702 4704 4707 4708 4712 4714 4715 4716 4717
3 2 3 2 1 3 3 3 3 2 2 2 1
4722 4724 4729 4730 4734 4735 4737 4739 4740 4742 4743 4745 4746
3 3 3 2 3 3 3 3 3 2 1 3 3
4749 4750 4753 4754 4757 4760 4764 4768 4769 4771 4772 4773 4775
3 3 1 3 1 1 2 1 1 1 2 3 2
4776 4777 4779 4780 4781 4782 4784 4785 4790 4793 4796 4797 4800
3 3 3 3 1 3 3 1 1 3 3 3 3
4802 4804 4806 4807 4813 4814 4816 4817 4819 4820 4821 4824 4826
1 1 1 3 2 1 1 3 1 3 3 3 1
4830 4831 4833 4834 4837 4838 4839 4841 4843 4845 4847 4849 4850
3 3 3 2 1 3 3 3 2 3 3 1 3
4851 4852 4854 4856 4857 4858 4862 4864 4865 4866 4868 4869 4873
3 3 2 3 3 3 3 2 2 1 3 1 3
4874 4878 4879 4882 4886 4887 4889 4892 4894 4896 4897 4899 4900
3 3 3 1 3 3 1 1 2 3 2 3 3
4901 4904 4905 4906 4908 4909 4910 4911 4912 4913 4914 4916 4917
3 3 2 3 3 3 2 3 3 3 3 1 3
4918 4919 4920 4921 4924 4925 4926 4927 4928 4929 4931 4932 4936
3 3 3 2 2 2 1 3 3 3 3 2 1
4939 4940 4943 4944 4950 4951 4952 4953 4955 4957 4959 4961 4962
3 3 3 2 3 2 3 3 3 2 2 1 2
4964 4965 4966 4967 4968 4971 4972 4974 4976 4977 4979 4981 4982
3 1 1 3 3 3 3 3 2 3 3 1 1
4983 4984 4986 4988 4991 4992 4995 4997 4998 4999 5001 5007 5009
1 3 3 3 1 3 3 1 3 3 3 3 3
5010 5011 5015 5016 5017 5018 5021 5024 5025 5026 5027 5028 5030
3 1 1 3 1 3 3 3 3 3 3 3 3
5031 5032 5036 5039 5041 5044 5045 5046 5047 5048 5051 5052 5053
1 3 1 1 1 3 1 3 2 3 3 2 3
5054 5057 5061 5062 5063 5064 5066 5067 5068 5070 5074 5077 5078
1 1 3 1 2 3 3 3 3 3 2 3 3
5079 5081 5083 5084 5085 5086 5087 5088 5090 5091 5094 5096 5097
3 3 3 2 2 2 2 1 2 3 3 1 2
5099 5101 5102 5106 5109 5111 5113 5116 5119 5120 5124 5125 5126
3 3 3 3 1 1 3 1 3 3 1 3 2
5127 5128 5129 5130 5132 5133 5135 5137 5138 5139 5142 5143 5144
2 2 3 3 3 3 3 3 1 2 2 1 3
5145 5148 5150 5152 5153 5154 5155 5156 5157 5159 5164 5165 5166
3 3 2 1 1 1 2 3 2 2 2 1 1
5167 5170 5171 5172 5174 5175 5179 5181 5185 5187 5188 5189 5190
3 1 3 3 3 2 3 2 3 3 1 2 2
5192 5193 5194 5201 5202 5204 5205 5207 5209 5210 5213 5214 5216
1 2 2 3 3 1 3 1 1 2 2 1 3
5218 5223 5224 5227 5233 5235 5236 5239 5240 5243 5245 5247 5248
1 1 2 3 1 3 3 3 3 1 3 3 3
5250 5251 5253 5254 5259 5262 5264 5265 5266 5273 5275 5277 5278
1 1 1 1 3 1 2 1 3 2 3 3 2
5284 5286 5287 5288 5290 5292 5293 5294 5295 5298 5300 5301 5302
3 2 3 3 3 3 2 3 2 3 3 2 1
5303 5305 5306 5307 5311 5313 5315 5316 5320 5321 5322 5325 5326
3 1 3 3 3 1 3 3 3 2 3 3 3
5327 5328 5329 5331 5334 5335 5336 5340 5343 5344 5345 5346 5348
3 3 3 3 1 3 1 3 3 1 1 3 3
5350 5351 5352 5353 5354 5355 5356 5358 5359 5360 5363 5366 5367
3 2 3 1 3 3 3 3 3 1 3 1 3
5368 5369 5371 5373 5375 5376 5377 5379 5381 5382 5383 5387 5389
3 2 3 3 2 1 3 3 3 1 3 3 1
5390 5391 5393 5394 5395 5396 5397 5398 5399 5400 5401 5404 5405
3 3 3 3 2 3 3 3 3 3 3 3 3
5407 5408 5409 5411 5412 5413 5417 5418 5420 5421 5423 5425 5427
2 3 2 3 3 2 2 3 3 3 3 1 1
5428 5429 5430 5431 5433 5434 5437 5438 5439 5441 5442 5443 5444
3 3 3 2 3 3 3 3 3 3 1 3 2
5446 5451 5453 5454 5455 5457 5458 5459 5460 5461 5464 5466 5467
1 1 3 1 3 3 3 3 1 3 3 3 3
5468 5469 5472 5473 5474 5475 5481 5485 5486 5487 5488 5489 5491
1 3 3 1 1 3 1 3 3 1 1 3 3
5493 5494 5495 5498 5499 5506 5509 5510 5511 5514 5516 5517 5518
1 1 1 1 3 1 1 1 3 3 3 1 3
5519 5521 5522 5525 5527 5533 5536 5537 5538 5540 5543 5544 5548
3 1 3 3 1 1 1 3 1 3 3 2 3
5549 5551 5554 5556 5558 5559 5563 5566 5567 5568 5569 5571 5574
1 2 3 3 3 2 3 3 2 2 3 2 3
5575 5576 5577 5578 5580 5581 5583 5584 5587 5589 5590 5591 5592
3 1 2 2 2 2 3 1 2 2 2 2 2
5593 5596 5597 5598 5601 5603 5604 5605 5608 5609 5610 5611 5612
2 2 2 2 2 2 2 2 3 2 2 2 2
5613 5617 5619 5621 5623 5624 5627 5628 5630 5631 5635 5638 5641
1 1 1 1 1 1 1 1 3 1 1 3 3
5643 5645 5646 5647 5652 5655 5656 5657 5658 5660 5662 5663 5664
1 1 1 1 3 1 1 1 1 1 3 3 3
5665 5668 5670 5671 5674 5675 5678 5679 5681 5683 5684 5687 5688
3 1 1 3 1 2 3 1 3 3 3 3 3
5690 5691 5692 5693 5694 5695 5696 5697 5698 5699 5703 5705 5710
3 3 1 3 3 1 3 3 3 3 2 1 2
5711 5715 5716 5717 5718 5720 5721 5723 5726 5727 5728 5729 5730
3 1 3 1 1 3 3 2 3 3 2 2 1
5732 5733 5735 5737 5738 5740 5741 5743 5745 5746 5750 5751 5753
3 2 2 3 2 1 2 1 1 2 2 1 1
5756 5757 5760 5761 5766 5769 5772 5774 5779 5780 5781 5783 5784
1 3 2 3 2 2 2 2 2 1 3 2 2
5788 5789 5790 5792 5793 5795 5796 5799 5803 5807 5809 5812 5813
1 2 2 1 3 1 2 2 2 2 2 2 2
5814 5815 5816 5817 5818 5822 5824 5827 5829 5830 5831 5832 5833
2 2 1 2 1 1 1 2 2 2 2 1 2
5835 5838 5840 5842 5843 5844 5845 5846 5847 5850 5851 5856 5857
2 1 2 2 3 1 2 1 2 2 2 1 2
5858 5859 5863 5864 5866 5869 5870 5871 5872 5874 5875 5878 5879
2 2 2 1 2 2 2 2 1 2 2 2 1
5880 5884 5885 5886 5887 5889 5890 5891 5892 5893 5894 5895 5900
1 1 2 2 1 2 1 2 1 1 1 1 1
5904 5906 5909 5910 5911 5913 5914 5921 5923 5926 5928 5930 5931
2 2 1 2 2 2 2 1 2 2 2 1 1
5932 5934 5936 5937 5938 5940 5943 5944 5946 5949 5950 5951 5952
2 2 1 2 1 2 1 2 2 3 2 2 2
5954 5957 5958 5959 5960 5961 5964 5965 5969 5972 5975 5976 5978
1 2 1 2 2 2 2 1 3 1 2 2 2
5980 5986 5987 5989 5990 5992 5993 5994 5995 5997 6005 6006 6009
3 1 2 1 2 2 2 2 1 1 2 1 2
6010 6013 6014 6015 6017 6019 6020 6021 6022 6025 6027 6029 6030
1 2 1 2 1 1 2 2 2 3 2 2 1
6031 6035 6036 6037 6038 6041 6042 6043 6046 6047 6053 6055 6056
2 2 2 1 2 1 2 2 1 1 2 1 2
6060 6063 6064 6065 6066 6067 6068 6069 6070 6071 6072 6076 6077
2 2 1 1 1 2 1 2 2 2 2 1 2
6078 6079 6082 6084 6085 6086 6087 6088 6090 6093 6098 6099 6100
2 2 2 2 1 2 3 1 2 1 2 2 1
6101 6104 6106 6107 6108 6109 6110 6111 6115 6116 6118 6120 6122
1 2 1 2 2 2 1 1 2 1 2 2 1
6126 6127 6128 6129 6131 6136 6137 6139 6140 6141 6143 6145 6147
2 2 2 2 2 2 2 2 2 2 2 1 2
6148 6149 6150 6151 6152 6154 6155 6158 6160 6163 6165 6166 6167
1 1 2 2 2 1 2 2 1 1 2 1 2
6169 6170 6172 6177 6179 6180 6181 6183 6186 6188 6191 6195 6200
1 2 2 1 2 1 1 2 1 2 1 2 2
6205 6206 6208 6210 6211 6212 6213 6214 6216 6217 6218 6220 6223
2 2 2 2 2 1 2 1 1 2 1 2 2
6224 6225 6226 6229 6230 6231 6232 6234 6235 6236 6237 6238 6239
2 1 2 1 2 1 1 2 1 2 2 1 2
6240 6241 6242 6243 6244 6245 6246 6248 6251 6252 6255 6256 6257
1 1 2 2 2 2 2 2 2 2 1 2 2
6259 6260 6261 6263 6264 6265 6266 6267 6269 6271 6272 6274 6277
1 1 1 1 2 2 1 1 2 1 2 2 2
6278 6279 6280 6282 6283 6284 6287 6290 6292 6293 6294 6295 6296
1 2 1 1 2 1 1 1 1 2 1 2 2
6297 6298 6300 6301 6303 6304 6308 6313 6315 6320 6321 6323 6328
2 2 2 2 2 2 1 2 2 2 2 2 2
6331 6332 6334 6335 6336 6337 6338 6340 6342 6344 6345 6348 6349
2 2 2 1 2 1 2 2 1 2 2 1 1
6351 6352 6354 6357 6358 6359 6362 6364 6365 6366 6369 6372 6373
1 2 2 2 1 1 1 1 2 2 1 3 2
6375 6376 6377 6380 6381 6383 6384 6385 6386 6387 6390 6391 6392
2 1 1 1 1 1 2 2 2 2 1 1 2
6393 6394 6395 6396 6397 6400 6403 6404 6405 6407 6409 6410 6413
1 2 1 2 2 2 2 2 2 1 1 2 2
6414 6416 6417 6419 6422 6425 6428 6429 6430 6431 6432 6433 6434
2 1 2 2 2 1 1 1 2 2 1 1 2
6435 6437 6439 6440 6441 6443 6444 6446 6447 6450 6451 6452 6453
2 1 2 2 1 2 2 1 1 1 2 2 1
6455 6457 6458 6459 6460 6461 6463 6466 6469 6471 6472 6473 6476
1 1 2 2 2 2 1 1 2 1 1 2 2
6477 6479 6480 6481 6482 6483 6484 6486 6487 6488 6491 6493 6494
2 1 2 2 1 2 1 2 1 2 2 1 2
6495 6496 6497 6498 6503 6506 6507 6513 6514 6516 6518 6521 6525
2 2 2 2 2 2 2 2 1 2 2 2 1
6526 6527 6529 6530 6532 6533 6534 6535 6536 6540 6544 6548 6549
2 2 2 2 1 2 2 2 1 2 1 1 2
6550 6551 6553 6555 6556 6557 6562 6563 6564 6565 6567 6569 6573
1 2 2 1 3 1 2 2 2 1 2 1 2
6577 6584 6586 6587 6589 6591 6592 6593 6594 6595 6598 6602 6603
1 2 2 1 2 1 1 2 1 2 2 1 1
6605 6606 6609 6610 6612 6614 6615 6616 6618 6619 6623 6624 6625
2 1 1 2 1 1 1 2 1 2 2 2 1
6626 6627 6630 6632 6634 6635 6638 6640 6641 6642 6644 6645 6647
2 1 2 2 1 1 2 3 2 2 2 2 2
6651 6655 6656 6657 6659 6661 6664 6665 6666 6669 6670 6671 6673
1 3 2 3 3 3 3 2 3 3 3 2 2
6677 6681 6684 6686 6687 6688 6691 6692 6694 6696 6697 6698 6699
3 1 3 2 2 2 2 2 2 3 2 2 2
6700 6702 6706 6707 6708 6710 6711 6712 6713 6714 6715 6716 6717
3 3 2 2 2 2 3 2 2 2 2 2 2
6719 6720 6721 6725 6727 6728 6729 6730 6731 6732 6735 6736 6740
2 3 3 3 3 3 2 2 3 2 1 3 2
6743 6744 6745 6749 6753 6755 6757 6758 6760 6762 6763 6765 6767
2 3 2 2 2 3 2 2 3 3 2 2 2
6769 6770 6771 6772 6773 6780 6781 6783 6785 6786 6787 6789 6790
2 2 3 2 2 3 2 1 3 3 3 2 2
6791 6794 6795 6796 6797 6798 6799 6800 6804 6805 6806 6810 6812
3 2 2 2 3 2 2 3 3 3 2 2 3
6813 6816 6823 6824 6825 6828 6829 6830 6831 6836 6839 6841 6842
1 2 2 2 2 2 2 3 2 2 2 3 2
6845 6847 6852 6853 6857 6858 6860 6861 6862 6866 6867 6868 6869
2 2 2 2 2 2 2 2 3 2 2 2 3
6871 6872 6874 6878 6879 6881 6882 6884 6886 6887 6888 6889 6891
2 3 2 3 2 2 2 2 3 2 3 3 2
6892 6893 6895 6899 6903 6905 6906 6908 6910 6911 6913 6914 6916
2 3 2 2 3 1 1 1 1 1 3 3 1
6920 6922 6923 6927 6928 6929 6930 6931 6935 6936 6938 6939 6941
3 3 3 1 3 1 1 1 1 1 3 1 1
6943 6944 6945 6946 6948 6950 6953 6954 6955 6959 6960 6961 6962
1 3 3 1 3 3 1 3 3 3 1 3 1
6963 6965 6968 6969 6970 6972 6973 6974 6978 6979 6980 6983 6985
3 1 3 1 1 3 2 3 3 3 2 1 3
6986 6987 6991 6992 6997 6998 6999 7003 7004 7005 7006 7007 7009
3 1 1 1 3 1 1 1 1 3 1 1 1
7011 7014 7015 7016 7017 7018 7019 7021 7022 7023 7027 7030 7032
1 1 3 3 3 1 1 3 2 3 1 3 3
7034 7036 7039 7040 7041 7042 7043 7044 7045 7048 7049 7052 7053
2 3 2 3 2 3 3 2 3 2 3 2 3
7054 7055 7059 7061 7062 7064 7065 7067 7070 7071 7072 7075 7076
3 2 3 2 2 2 2 2 3 3 2 2 2
7077 7078 7079 7081 7082 7085 7086 7088 7089 7094 7099 7101 7102
2 2 3 3 2 2 2 2 3 2 2 2 3
7105 7106 7108 7109 7110 7111 7113 7115 7117 7119 7120 7121 7122
2 2 2 3 3 3 3 2 2 2 3 2 2
7124 7125 7126 7128 7129 7131 7135 7137 7138 7142 7143 7144 7147
2 2 2 2 2 2 2 2 3 3 3 2 3
7151 7152 7153 7158 7163 7169 7170 7175 7178 7179 7180 7181 7183
3 3 2 2 2 2 2 3 2 2 3 2 3
7185 7186 7187 7188 7189 7191 7193 7195 7196 7199 7201 7202 7203
2 2 3 2 2 3 3 3 2 2 2 3 2
7208 7209 7211 7212 7215 7217 7219 7222 7226 7227 7229 7230 7233
3 2 2 3 3 2 3 3 3 3 2 3 3
7234 7236 7238 7241 7245 7246 7247 7249 7250 7253 7258 7259 7261
2 2 2 3 3 2 2 2 2 2 2 2 2
7262 7264 7266 7270 7272 7273 7274 7275 7277 7279 7281 7282 7286
2 3 3 2 2 2 2 2 2 2 2 2 2
7287 7296 7300 7304 7306 7307 7310 7311 7312 7315 7316 7317 7319
2 2 3 2 3 2 2 2 2 2 2 2 2
7321 7322 7326 7329 7330 7331 7335 7336 7337 7338 7339 7344 7347
2 2 3 1 1 1 1 3 1 3 1 3 1
7350 7351 7354 7356 7357 7358 7360 7361 7363 7366 7367 7368 7369
1 3 1 1 1 1 3 1 1 1 1 1 1
7370 7371 7373 7374 7375 7376 7377 7383 7385 7386 7387 7389 7390
1 1 1 1 1 1 1 1 1 1 1 1 3
7393 7395 7399 7400 7402 7403 7404 7406 7407 7409 7411 7412 7415
1 1 1 3 1 1 1 1 1 1 1 1 1
7416 7417 7421 7422 7423 7424 7425 7426 7427 7428 7429 7430 7431
1 3 3 1 3 3 3 1 3 1 1 1 3
7432 7434 7438 7442 7443 7446 7448 7449 7450 7453 7454 7455 7459
3 1 3 1 1 1 1 3 3 3 1 1 1
7461 7462 7464 7466 7473 7475 7477 7479 7480 7483 7484 7485 7489
1 1 1 1 3 1 1 1 1 3 3 1 3
7490 7493 7494 7495 7498 7500 7501 7503 7504 7506 7510 7513 7515
1 1 1 1 3 3 3 3 3 2 3 2 2
7518 7519 7522 7523 7524 7527 7528 7530 7531 7532 7534 7536 7544
3 3 3 3 3 1 3 2 3 2 3 2 2
7545 7546 7548 7549 7550 7551 7552 7553 7554 7555 7556 7557 7558
3 3 1 1 3 3 3 3 2 3 3 3 1
7559 7560 7563 7564 7566 7569 7570 7573 7579 7582 7583 7584 7586
1 3 3 2 3 3 3 3 3 2 2 3 3
7588 7589 7591 7595 7601 7606 7608 7609 7610 7613 7615 7620 7621
2 3 2 3 3 3 2 3 1 3 3 2 1
7622 7626 7627 7630 7632 7634 7635 7636 7637 7639 7641 7642 7644
3 3 2 2 2 3 1 2 3 3 2 3 2
7647 7649 7652 7653 7654 7656 7658 7662 7666 7667 7668 7670 7671
3 2 1 2 3 3 3 2 1 3 2 2 2
7672 7673 7675 7678 7683 7684 7685 7686 7688 7690 7691 7694 7700
3 3 3 3 1 3 1 2 1 3 2 1 2
7703 7706 7707 7709 7710 7712 7713 7715 7716 7717 7720 7721 7722
3 3 2 2 2 1 2 2 2 2 1 3 2
7723 7725 7728 7729 7731 7732 7733 7735 7736 7737 7738 7740 7741
3 2 2 3 2 2 2 1 2 2 1 1 2
7746 7747 7750 7751 7752 7757 7760 7761 7762 7766 7772 7773 7775
3 3 1 3 3 2 1 3 3 1 2 2 3
7777 7778 7779 7783 7784 7785 7787 7788 7789 7791 7792 7793 7795
2 2 1 3 3 3 2 1 2 2 1 1 1
7796 7799 7800 7805 7809 7812 7815 7816 7819 7821 7822 7823 7825
2 1 1 2 1 2 1 2 2 1 2 2 1
7829 7838 7839 7840 7841 7842 7843 7844 7846 7847 7848 7849 7850
1 2 2 1 3 2 2 2 1 3 2 2 1
7851 7853 7854 7855 7856 7857 7858 7859 7862 7865 7868 7869 7871
2 2 3 3 3 2 2 3 2 3 1 1 2
7874 7875 7876 7877 7878 7879 7880 7882 7884 7885 7887 7890 7891
2 2 2 2 1 2 2 2 3 3 1 1 1
7892 7893 7898 7899 7900 7901 7902 7904 7907 7908 7909 7910 7912
2 1 2 2 1 1 2 3 1 2 2 2 2
7913 7915 7916 7919 7923 7924 7925 7926 7927 7929 7930 7931 7932
1 2 3 3 2 2 3 1 2 1 2 2 1
7936 7937 7938 7939 7940 7943 7945 7947 7948 7949 7950 7953 7954
2 2 1 2 1 1 3 2 3 3 2 1 3
7955 7957 7963 7964 7966 7967 7969 7973 7974 7975 7976 7981 7985
1 1 2 1 1 2 2 1 3 2 2 2 1
7989 7992 7994 7996 7997 7998 8000 8001 8002 8003 8004 8006 8008
1 2 3 1 2 2 2 2 2 2 2 2 2
8012 8015 8017 8018 8022 8023 8025 8026 8028 8029 8033 8034 8037
2 2 1 2 2 1 2 2 2 1 2 2 1
8038 8042 8044 8045 8048 8049 8052 8054 8055 8056 8057 8058 8059
2 2 2 2 1 2 2 2 2 2 2 1 2
8061 8062 8063 8066 8069 8071 8072 8076 8077 8078 8080 8081 8082
1 2 1 2 2 1 2 1 2 2 1 2 2
8083 8084 8085 8088 8090 8093 8094 8098 8099 8100 8102 8104 8107
1 1 2 2 1 3 1 2 2 1 2 1 2
8109 8111 8112 8113 8114 8116 8117 8118 8123 8124 8126 8127 8129
1 1 1 1 1 2 2 2 2 2 1 1 2
8130 8131 8132 8135 8141 8142 8143 8144 8147 8148 8149 8151 8152
1 2 2 2 2 1 1 2 2 2 1 2 2
8153 8155 8156 8159 8160 8161 8162 8163 8165 8169 8170 8171 8176
2 2 2 2 2 2 1 2 2 1 2 1 1
8177 8179 8181 8183 8184 8186 8187 8189 8190 8194 8196 8197 8199
1 3 2 1 2 2 1 3 2 1 1 2 2
8201 8206 8208 8209 8210 8212 8213 8215 8217 8218 8220 8221 8222
2 2 1 2 1 1 1 2 1 2 2 2 2
8223 8225 8227 8230 8233 8234 8235 8236 8238 8239 8240 8243 8244
2 1 2 1 1 1 2 2 2 2 2 2 2
8245 8246 8248 8249 8251 8252 8253 8256 8258 8259 8260 8263 8264
2 2 2 2 1 1 1 2 1 1 2 2 2
8268 8269 8270 8271 8274 8275 8277 8279 8280 8281 8283 8284 8285
2 1 1 2 2 2 2 2 1 2 2 2 2
8286 8287 8289 8290 8291 8293 8294 8296 8299 8301 8302 8303 8305
2 1 2 1 2 1 1 2 1 2 2 2 1
8306 8308 8309 8312 8314 8315 8318 8319 8320 8321 8322 8323 8329
1 2 1 2 2 1 2 2 2 1 2 2 2
8330 8332 8333 8334 8335 8337 8338 8339 8340 8343 8344 8346 8347
1 2 2 2 1 2 2 2 2 2 2 1 2
8353 8354 8356 8358 8364 8366 8370 8371 8372 8373 8374 8375 8376
1 2 1 2 1 2 1 3 1 2 2 1 2
8377 8378 8379 8380 8381 8382 8384 8385 8389 8390 8394 8395 8396
1 2 2 1 2 1 2 2 2 1 1 2 2
8399 8400 8401 8402 8403 8404 8406 8412 8413 8415 8416 8419 8420
2 1 1 2 1 1 1 1 2 2 2 1 1
8421 8423 8427 8428 8429 8431 8432 8435 8437 8438 8439 8440 8442
1 1 1 2 2 2 1 2 2 2 2 2 2
8445 8446 8447 8449 8451 8452 8458 8459 8461 8462 8463 8465 8469
2 2 2 2 1 2 1 2 2 2 2 2 1
8471 8472 8473 8474 8476 8479 8480 8481 8482 8484 8485 8486 8488
1 2 1 1 1 2 2 2 1 1 2 1 2
8491 8493 8494 8495 8498 8500 8502 8504 8505 8506 8507 8508 8509
2 2 1 2 1 2 1 2 2 2 1 2 1
8511 8512 8514 8515 8516 8517 8519 8527 8528 8529 8530 8533 8535
1 2 2 1 1 3 1 2 2 1 2 1 2
8536 8541 8543 8544 8547 8548 8550 8551 8552 8555 8556 8562 8563
2 1 1 2 2 2 1 2 1 1 1 1 2
8565 8567 8568 8569 8571 8573 8574 8575 8577 8578 8580 8581 8582
2 2 2 2 1 2 2 2 2 2 1 2 2
8583 8584 8585 8587 8591 8593 8596 8598 8600 8602 8603 8604 8605
1 1 2 1 2 2 2 1 2 1 1 1 2
8610 8611 8613 8615 8616 8617 8618 8619 8620 8621 8622 8623 8627
2 2 2 2 1 2 2 2 1 2 2 2 2
8628 8629 8631 8632 8633 8635 8636 8639 8640 8641 8642 8647 8649
3 2 3 2 2 2 1 1 1 2 2 2 2
8651 8652 8654 8655 8658 8659 8660 8661 8662 8663 8665 8666 8667
2 2 1 2 2 2 2 2 2 2 1 2 2
8669 8671 8673 8674 8675 8677 8681 8683 8686 8690 8691 8693 8694
1 1 1 1 2 1 2 2 1 1 1 1 2
8695 8696 8699 8703 8704 8705 8706 8708 8709 8710 8711 8714 8716
2 1 2 1 2 2 2 2 2 1 2 3 2
8717 8718 8719 8724 8726 8728 8731 8735 8737 8744 8745 8747 8748
1 2 2 1 1 2 1 2 2 2 2 2 2
8749 8750 8751 8754 8756 8757 8759 8760 8762 8763 8764 8766 8767
2 1 1 1 3 2 2 2 2 2 1 2 2
8769 8770 8771 8773 8777 8783 8784 8785 8788 8790 8793 8794 8799
2 2 1 2 2 1 2 2 2 2 2 2 1
8801 8802 8803 8804 8807 8809 8810 8811 8814 8815 8817 8818 8820
2 1 3 1 1 1 2 2 1 2 2 2 2
8824 8826 8829 8831 8833 8834 8835 8836 8838 8839 8841 8842 8846
1 2 3 2 2 3 1 1 2 2 1 1 2
8847 8848 8850 8852 8853 8855 8858 8862 8863 8867 8868 8872 8873
1 1 2 1 1 1 2 2 1 1 1 2 1
8876 8880 8881 8884 8885 8888 8892 8893 8894 8895 8899 8901 8902
1 1 1 1 2 2 1 1 1 1 1 1 2
8903 8905 8906 8907 8909 8910 8912 8914 8915 8916 8920 8921 8926
1 1 1 2 1 2 2 2 2 2 1 2 2
8927 8930 8931 8932 8933 8934 8935 8937 8940 8941 8942 8945 8948
1 1 3 2 1 2 1 2 2 1 1 2 1
8954 8955 8957 8958 8960 8963 8964 8966 8967 8968 8972 8973 8974
2 2 2 1 2 2 1 2 2 2 2 2 2
8977 8978 8981 8985 8989 8991 8992 8993 8994 8995 8996 8997 8999
2 2 1 2 3 2 2 2 1 2 2 1 1
9000 9001 9002 9003 9004 9005 9007 9008 9009 9012 9014 9016 9017
1 2 2 1 2 2 2 1 2 1 1 2 1
9018 9020 9021 9022 9024 9027 9028 9030 9031 9032 9035 9036 9037
2 2 2 2 1 2 2 2 2 1 2 2 1
9038 9039 9042 9043 9046 9048 9049 9050 9051 9054 9055 9058 9059
2 2 1 1 2 1 2 1 1 2 2 2 1
9061 9063 9070 9071 9074 9076 9079 9082 9083 9084 9085 9087 9088
2 1 2 2 1 1 2 1 1 2 1 1 2
9091 9092 9094 9096 9098 9102 9103 9105 9107 9108 9109 9113 9114
2 1 1 1 2 2 1 1 2 2 1 2 1
9116 9117 9118 9120 9124 9125 9126 9128 9130 9132 9134 9136 9137
1 1 1 2 2 1 2 1 1 2 2 2 2
9138 9141 9142 9146 9147 9155 9156 9158 9160 9162 9168 9171 9172
2 2 2 2 1 1 1 2 2 2 1 1 2
9173 9174 9175 9176 9177 9178 9179 9180 9182 9184 9186 9187 9188
1 1 2 1 2 1 2 2 1 2 1 2 1
9194 9197 9198 9203 9204 9205 9206 9207 9208 9209 9210 9211 9212
1 1 2 3 2 2 2 1 2 2 2 2 1
9213 9214 9215 9218 9220 9221 9224 9225 9226 9227 9228 9229 9231
2 1 1 1 1 1 3 1 3 1 2 3 3
9232 9235 9239 9241 9242 9244 9245 9247 9248 9249 9250 9252 9253
2 1 2 2 1 2 1 2 2 2 2 2 2
9255 9256 9261 9262 9263 9265 9267 9269 9270 9272 9274 9275 9276
2 1 2 1 1 2 2 2 2 2 1 2 2
9277 9280 9281 9285 9289 9295 9296 9297 9300 9303 9305 9306 9307
2 2 1 1 3 2 2 2 2 2 2 1 2
9308 9309 9313 9314 9315 9316 9317 9318 9319 9320 9322 9326 9328
2 2 2 2 2 2 1 1 2 1 2 1 2
9329 9331 9332 9333 9334 9335 9336 9338 9339 9340 9341 9342 9343
1 2 1 2 2 2 2 1 1 1 2 1 1
9344 9345 9346 9348 9351 9352 9353 9356 9357 9358 9359 9360 9362
1 2 2 2 1 2 2 1 2 1 1 1 2
9364 9365 9367 9368 9369 9370 9372 9373 9374 9375 9378 9379 9381
2 2 2 2 2 2 3 1 2 1 2 2 2
9382 9384 9385 9386 9387 9388 9389 9391 9392 9393 9394 9395 9396
1 1 1 1 2 2 1 1 1 1 2 2 1
9397 9398 9399 9402 9403 9406 9407 9411 9413 9415 9416 9417 9418
2 2 1 1 2 2 2 1 1 2 2 1 2
9419 9421 9422 9423 9424 9425 9426 9428 9429 9430 9431 9432 9436
1 2 2 2 2 1 2 1 2 2 1 2 2
9438 9439 9442 9444 9447 9452 9453 9454 9455 9457 9461 9463 9465
1 2 1 2 1 2 1 2 1 2 2 2 2
9467 9470 9471 9476 9480 9481 9482 9483 9484 9485 9487 9489 9490
1 2 2 2 2 1 1 2 2 1 1 2 2
9491 9495 9498 9499 9500 9502 9503 9505 9506 9507 9508 9509 9511
2 3 2 1 1 2 2 2 2 2 2 1 2
9512 9513 9514 9517 9518 9519 9522 9525 9526 9528 9530 9531 9532
3 1 2 1 1 1 1 1 2 2 2 1 1
9535 9537 9538 9539 9543 9545 9546 9547 9548 9551 9553 9554 9558
2 2 2 2 2 2 2 2 2 2 1 1 2
9561 9562 9563 9564 9568 9572 9573 9575 9576 9577 9578 9579 9582
1 1 1 2 2 1 2 1 1 2 2 2 1
9583 9585 9587 9588 9590 9592 9597 9598 9599 9600 9603 9604 9608
2 2 1 2 2 1 2 2 2 1 2 2 2
9609 9611 9612 9613 9616 9617 9618 9619 9620 9623 9624 9625 9627
2 1 3 2 1 1 2 1 1 1 2 2 2
9628 9631 9634 9635 9636 9638 9640 9641 9643 9648 9650 9652 9653
2 1 1 2 2 1 2 2 1 1 2 2 1
9654 9656 9658 9659 9661 9662 9663 9664 9666 9667 9669 9670 9673
2 2 2 2 2 2 2 1 1 1 1 2 1
9676 9682 9685 9686 9687 9688 9689 9690 9693 9696 9697 9699 9700
1 2 1 2 2 2 2 2 2 1 2 2 2
9701 9705 9708 9710 9711 9712 9713 9714 9715 9717 9718 9719 9720
2 2 2 2 2 2 2 1 2 2 1 2 2
9723 9724 9725 9726 9727 9728 9736 9737 9741 9743 9744 9746 9747
1 1 1 2 2 2 1 2 2 1 1 1 2
9748 9749 9751 9752 9754 9756 9757 9758 9759 9761 9762 9765 9766
2 1 2 1 2 1 1 2 2 2 2 2 2
9767 9768 9769 9771 9774 9776 9777 9778 9780 9783 9786 9787 9789
1 2 1 1 2 2 2 2 2 2 2 1 1
9790 9793 9795 9796 9797 9798 9799 9801 9802 9803 9804 9806 9807
1 1 2 2 1 2 1 2 2 1 2 1 2
9808 9810 9812 9813 9814 9815 9817 9819 9820 9823 9824 9826 9829
1 2 2 2 1 2 2 1 2 2 1 1 2
9832 9834 9835 9837 9840 9842 9843 9845 9846 9849 9850 9852 9855
2 2 2 2 1 1 2 2 2 2 1 1 1
9857 9860 9861 9863 9869 9870 9872 9873 9875 9876 9878 9879 9882
2 2 2 2 2 2 1 1 1 2 2 1 2
9883 9884 9885 9888 9890 9891 9892 9893 9894 9895 9896 9897 9898
1 1 2 2 2 2 1 1 2 2 2 2 2
9899 9902 9903 9904 9906 9907 9911 9912 9913 9914 9916 9917 9918
2 2 1 2 2 2 2 2 2 1 2 2 2
9919 9922 9923 9928 9931 9932 9933 9935 9939 9940 9941 9943 9945
2 2 1 1 2 2 2 2 1 2 2 2 1
9946 9948 9949 9950 9951 9953 9954 9957 9959 9960 9962 9966 9967
2 2 1 2 1 1 1 2 1 2 2 2 1
9968 9973 9974 9975 9977 9981 9982 9983 9984 9985 9988 9989 9990
2 2 2 1 1 2 1 2 2 1 2 1 1
9991 9992 9993 9996 9997 9998 10002 10004 10006 10007 10008 10009 10011
2 2 2 2 2 2 1 2 1 2 2 2 1
10013 10016 10017 10019 10020 10021 10025 10026 10028 10030 10031 10032 10033
2 1 1 2 2 1 2 2 2 1 1 1 2
10034 10036 10040 10041 10047 10048 10051 10052 10058 10059 10060 10061 10062
2 1 1 2 1 1 1 1 1 2 2 1 2
10063 10064 10065 10066 10069 10070 10071 10072 10074 10075 10078 10079 10081
2 2 1 1 1 1 2 2 1 2 2 2 1
10083 10091 10092 10093 10095 10096 10098 10105 10107 10108 10109 10110 10111
2 1 1 2 1 2 1 1 2 2 2 2 2
10113 10115 10117 10119 10120 10121 10122 10123 10124 10125 10126 10127 10129
2 1 2 1 1 2 2 2 2 2 1 2 2
10130 10133 10136 10139 10141 10144 10145 10146 10147 10149 10150 10151 10152
2 2 2 1 2 2 2 1 1 2 2 1 2
10154 10155 10156 10159 10162 10167 10168 10172 10177 10179 10182 10183 10184
2 2 1 2 1 1 1 2 2 2 2 1 2
10186 10187 10188 10189 10190 10192 10201 10202 10209 10211 10213 10220 10221
1 1 2 2 2 2 2 2 2 2 2 2 1
10222 10224 10225 10227 10228 10229 10230 10232 10236 10238 10242 10245 10246
1 2 2 1 1 2 1 1 1 1 2 2 2
10247 10250 10251 10252 10254 10258 10259 10260 10261 10263 10264 10265 10269
1 1 2 3 1 2 2 1 1 1 1 2 2
10270 10271 10274 10276 10278 10280 10282 10283 10286 10287 10288 10290 10291
2 1 2 2 1 2 2 2 2 2 2 1 1
10292 10295 10297 10302 10303 10304 10309 10313 10314 10316 10319 10321 10322
2 2 2 2 1 2 2 2 1 2 1 1 2
10323 10324 10325 10326 10331 10332 10333 10335 10336 10338 10339 10343 10346
2 2 1 2 1 1 2 2 1 2 2 2 1
10347 10351 10352 10353 10354 10357 10359 10362 10367 10368 10370 10372 10374
2 1 2 2 1 2 1 2 2 1 2 2 2
10375 10376 10378 10379 10380 10383 10385 10386 10388 10390 10392 10393 10394
2 2 1 2 1 2 2 2 2 1 2 2 1
10396 10397 10399 10400 10401 10406 10410 10413 10415 10418 10420 10421 10422
1 1 1 2 2 2 2 1 2 2 1 1 2
10427 10438 10440 10441 10445 10446 10447 10448 10452 10453 10454 10456 10457
2 1 1 2 1 1 1 2 2 1 2 2 2
10458 10459 10461 10462 10466 10467 10468 10469 10472 10474 10475 10476 10477
1 2 2 2 2 2 2 1 2 2 2 2 2
10478 10480 10481 10484 10486 10487 10491 10493 10495 10497 10498 10499 10500
2 1 2 1 2 2 1 2 2 2 2 2 2
10501 10504 10506 10507 10508 10510 10512 10514 10519 10523 10527 10528 10529
2 1 1 2 2 1 2 2 2 2 2 1 2
10531 10535 10537 10538 10539 10540 10541 10542 10543 10545 10549 10550 10551
2 2 1 1 1 2 2 2 1 1 1 1 2
10552 10555 10556 10558 10561 10563 10564 10565 10566 10568 10573 10576 10577
2 1 2 2 2 2 2 1 2 1 2 2 2
10578 10579 10581 10583 10586 10587 10588 10589 10591 10594 10595 10597 10598
2 1 1 2 1 2 2 2 2 2 2 1 2
10599 10600 10601 10604 10606 10608 10609 10610 10612 10614 10617 10618 10619
1 2 1 2 1 2 2 2 1 1 2 2 2
10622 10623 10626 10627 10629 10631 10632 10633 10635 10636 10637 10639 10641
1 1 2 2 2 1 2 2 2 1 2 2 1
10642 10643 10644 10645 10646 10648 10649 10650 10651 10653 10654 10655 10656
2 2 1 2 2 2 1 2 2 2 2 2 2
10657 10660 10664 10665 10666 10667 10668 10669 10670 10671 10672 10674 10676
2 1 2 1 2 1 1 2 2 2 2 1 1
10678 10680 10681 10683 10684 10687 10688 10695 10696 10697 10699 10700 10701
1 2 1 2 1 2 1 1 2 1 1 1 1
10702 10704 10706 10708 10714 10715 10716 10717 10719 10720 10722 10723 10725
1 2 2 1 2 2 1 1 2 1 1 1 2
10726 10727 10728 10729 10730 10733 10734 10736 10737 10739 10741 10746 10749
2 2 2 1 2 2 1 1 2 2 1 2 2
10754 10757 10758 10759 10760 10761 10764 10768 10771 10772 10776 10778 10780
2 2 1 2 1 2 2 2 2 2 1 2 1
10783 10786 10790 10791 10794 10795 10796 10800 10801 10802 10803 10806 10807
1 1 1 2 2 2 1 1 2 2 2 2 2
10815 10816 10817 10818 10819 10820 10823 10826 10827 10829 10830 10831 10832
2 1 2 1 1 1 1 1 1 2 1 2 2
10834 10835 10836 10839 10842 10849 10850 10852 10856 10858 10859 10863 10864
2 2 1 2 2 2 2 1 2 2 1 2 2
10865 10866 10867 10870 10872 10875 10876 10877 10879 10880 10883 10884 10888
2 1 1 2 2 2 2 1 1 2 1 1 1
10890 10891 10892 10893 10894
1 2 2 2 2
Within cluster sum of squares by cluster:
[1] 31109.24 30396.87 39887.82
(between_SS / total_SS = 28.6 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault"
#plot k-mean
fviz_cluster(list(data = data1, cluster = km$cluster),
ellipse.type = "norm", geom = "point", stand = FALSE,
palette = "jco", ggtheme = theme_classic())
In the provided cluster plot, the points are clustered into three groups, each with different sizes and locations. This suggests that the dataset is heterogeneous and does not easily fit into a single category. The size of each cluster can also be considered. The cluster with the largest number of points is cluster 1, accounting for approximately 89.7% of the total dataset. Cluster 2, which is significantly smaller than cluster 1, contains about 2.4% of the data. Finally, cluster 3 contains the remaining 0.9% of the data.
Based on the cluster sizes, we can say that cluster 1 is significantly larger and more influential than the other two clusters. However, the overall goodness or badness of the clustering result cannot be directly determined by the cluster sizes alone. Other factors such as the separation of clusters and the distribution of data points within each cluster also play a crucial role in evaluating the quality of the clustering.
It is important to note that the effectiveness of clustering techniques often depends on the specific context and the underlying assumptions about the data distribution. Therefore, in practice, one might need to try out different clustering algorithms and techniques to determine the best approach for their specific dataset.
#avg silhouette
library(cluster)
sil <- silhouette(km$cluster, dist(data))
rownames(sil) <- rownames(data)
fviz_silhouette(sil)
cluster size ave.sil.width 1 1 1732 -0.37 2 2 2107 0.35 3 3 2055 0.73
#Total within-cluster-sum of square
km$tot.withinss
cluster_assignments <- c(km$cluster)
ground_truth_labels <- c(dataset$satisfaction)
data <- data.frame(cluster = cluster_assignments, label = ground_truth_labels)
# Function to calculate BCubed precision and recall
calculate_bcubed_metrics <- function(data) {
n <- nrow(data)
precision_sum <- 0
recall_sum <- 0
for (i in 1:n) {
cluster <- data$cluster[i]
label <- data$label[i]
# Count the number of items from the same category within the same cluster
same_category_same_cluster <- sum(data$label[data$cluster == cluster] == label)
# Count the total number of items in the same cluster
total_same_cluster <- sum(data$cluster == cluster)
# Count the total number of items with the same category
total_same_category <- sum(data$label == label)
# Calculate precision and recall for the current item and add them to the sums
precision_sum <- precision_sum + same_category_same_cluster /total_same_cluster
recall_sum <- recall_sum + same_category_same_cluster / total_same_category
}
# Calculate average precision and recall
precision <- precision_sum / n
recall <- recall_sum / n
return(list(precision = precision, recall = recall))
}
# Calculate BCubed precision and recall
metrics <- calculate_bcubed_metrics(data)
# Extract precision and recall from the metrics
precision <- metrics$precision
recall <- metrics$recall
# Print the results
cat("BCubed Precision:", precision, "\n")
cat("BCubed Recall:", recall, "\n")
BCubed Precision: 0.6287017 BCubed Recall: 0.4258053
6.2.3 - cluster k=4 :¶
#calculate k-mean
km <- kmeans(data2, 4, iter.max = 140 , algorithm="Lloyd", nstart=100)
km
K-means clustering with 4 clusters of sizes 1813, 1332, 1389, 1360
Cluster means:
Gender Customer.Type Age Type.of.Travel Class Seat.comfort
1 1.462217 1.108660 0.4578350 1.675124 2.197463 4.024821
2 1.601351 1.345345 0.3830886 1.647147 1.687688 2.343844
3 1.503240 1.179266 0.4580708 1.737941 1.838013 2.771778
4 1.479412 1.202941 0.4518936 1.708824 2.173529 1.705882
Departure.Arrival.time.convenient Food.and.drink Inflight.wifi.service
1 4.011031 3.943740 3.726420
2 3.054805 2.596096 1.668919
3 2.732181 2.833693 3.482361
4 1.764706 1.584559 3.734559
Inflight.entertainment Online.support Ease.of.Online.booking On.board.service
1 4.132377 4.154440 4.249311 4.056260
2 2.261261 1.821321 1.892643 3.180931
3 3.316055 3.598992 3.002160 2.179266
4 3.283824 4.123529 4.293382 4.072794
Leg.room.service Baggage.handling Checkin.service Cleanliness Online.boarding
1 3.968560 4.218423 3.706564 4.187534 3.932708
2 3.269520 3.620120 3.012012 3.644895 1.666667
3 2.418287 2.486681 2.688985 2.520518 3.434845
4 4.005882 4.174265 3.619853 4.208088 3.984559
Arrival.Delay.in.Minutes
1 0.02063975
2 0.02685257
3 0.02457265
4 0.02286482
Clustering vector:
1 2 3 4 6 7 8 10 11 12 13 14 18
2 3 4 2 4 3 3 3 4 2 4 3 2
19 20 21 22 24 25 28 29 31 32 33 34 36
2 4 4 4 3 2 2 4 2 2 4 3 4
37 39 42 45 46 47 49 50 53 54 55 56 60
2 4 4 4 2 2 2 2 2 4 4 4 2
62 63 69 70 71 73 74 75 76 81 82 83 85
2 4 2 2 2 4 2 4 2 2 3 2 2
88 89 90 92 93 94 96 97 98 100 101 104 105
2 4 4 2 2 2 3 4 4 4 4 2 4
106 107 108 109 112 113 114 115 122 125 126 127 128
4 3 4 2 4 2 4 4 2 2 2 3 3
129 130 134 135 136 139 142 147 148 149 151 152 155
4 4 4 2 4 4 2 4 2 2 3 4 4
160 161 162 163 165 168 169 170 171 173 175 176 177
3 3 3 4 4 4 2 2 2 2 2 4 2
178 183 184 185 186 188 189 196 197 199 203 205 209
2 2 4 4 4 1 2 4 2 4 2 2 4
211 212 213 217 221 222 223 226 227 232 234 238 239
2 2 4 2 2 4 2 2 3 2 4 2 3
241 242 245 247 249 251 252 254 255 257 259 261 262
2 2 4 2 2 2 2 3 2 3 4 2 2
264 267 271 273 274 275 276 278 281 283 285 286 287
4 2 3 4 2 2 2 1 2 2 4 2 2
288 289 290 291 292 293 295 297 299 301 303 307 308
4 2 3 2 4 4 2 2 3 3 3 3 3
310 311 317 319 320 322 324 325 326 328 330 332 334
4 2 2 2 3 3 2 2 2 3 2 2 4
335 337 338 341 342 346 348 349 351 354 356 358 359
4 4 4 2 3 2 3 3 3 2 2 2 2
361 362 363 364 366 369 372 373 375 376 379 380 381
4 3 3 3 3 4 3 4 2 2 3 3 2
382 384 386 387 388 390 391 394 398 400 401 402 406
2 2 3 3 3 4 3 3 4 2 2 3 3
407 408 409 411 413 414 416 421 423 430 431 432 433
3 2 3 4 4 4 3 3 3 2 4 2 2
435 436 438 440 442 444 445 448 451 453 455 456 459
3 3 2 4 4 4 2 3 2 4 4 4 4
462 463 464 465 466 467 468 475 476 477 480 481 483
4 2 2 2 4 2 2 2 4 2 4 2 4
484 487 490 492 495 496 498 499 500 503 504 505 508
2 4 3 4 2 2 2 4 2 4 2 4 2
511 515 516 519 521 524 526 527 530 532 533 536 537
2 2 2 4 3 2 4 2 2 2 3 2 4
538 539 543 544 545 547 551 552 553 554 556 558 559
2 3 4 2 2 4 2 2 2 4 2 2 2
560 562 564 565 569 571 572 573 575 579 581 586 587
4 4 4 2 2 2 4 4 1 1 3 2 2
589 592 593 595 596 597 602 604 605 607 609 614 615
4 2 2 2 2 4 2 4 2 4 3 2 2
617 619 620 622 626 631 635 636 638 640 641 643 646
3 2 4 2 2 2 4 2 2 2 3 2 2
647 648 649 651 653 655 656 657 658 659 660 662 663
2 4 4 2 2 4 2 2 1 4 4 2 2
664 666 667 669 670 673 675 677 679 684 686 687 688
2 2 2 4 4 2 2 2 4 2 2 2 2
691 692 693 694 696 701 706 707 708 711 712 715 717
2 3 2 2 2 2 2 4 2 2 2 2 4
718 720 721 722 724 725 727 728 729 730 734 735 736
3 2 2 2 4 3 4 4 3 1 1 1 2
737 738 739 740 741 742 743 744 745 746 747 748 749
3 1 1 2 4 4 4 4 4 4 2 4 2
751 752 753 756 757 758 759 761 762 764 765 766 767
4 2 3 3 2 3 3 3 3 3 3 4 3
768 769 771 776 777 779 781 782 784 787 788 794 795
2 3 4 4 2 4 3 4 3 3 2 3 3
796 802 803 804 806 809 814 815 818 819 821 822 823
3 3 3 4 3 3 3 2 2 3 3 2 2
825 826 828 830 831 832 833 835 836 842 843 847 848
3 4 2 3 3 2 3 2 2 2 2 3 2
850 851 853 858 859 860 864 867 869 870 872 874 877
3 3 1 3 2 3 3 2 2 2 2 3 3
880 881 883 886 887 888 889 892 893 894 897 903 904
2 3 3 3 1 2 3 2 2 3 4 2 2
905 906 907 908 909 912 913 914 916 917 919 922 923
3 1 1 1 1 1 1 1 1 2 2 3 3
924 925 927 929 931 932 933 938 940 941 942 944 945
3 2 2 2 1 1 1 1 1 2 1 1 2
947 949 952 954 955 956 958 961 963 964 969 970 972
1 2 1 1 2 2 1 1 2 2 1 2 1
973 974 977 978 979 980 982 983 985 986 987 988 990
1 3 1 3 1 3 2 2 3 2 1 2 2
992 993 994 997 1000 1005 1007 1011 1015 1016 1018 1019 1021
2 3 1 3 2 1 1 1 1 1 2 1 3
1022 1023 1024 1025 1027 1028 1031 1032 1036 1037 1038 1039 1043
1 1 2 1 1 2 3 2 1 1 2 4 2
1048 1049 1050 1051 1052 1053 1054 1055 1057 1058 1061 1062 1063
2 1 2 2 1 1 1 2 1 1 1 2 1
1065 1066 1070 1071 1072 1076 1079 1080 1081 1082 1088 1091 1093
2 1 2 3 1 1 3 1 1 2 1 2 2
1095 1096 1098 1099 1101 1104 1109 1110 1112 1114 1115 1116 1118
1 2 1 1 2 2 1 1 1 2 1 3 1
1119 1120 1121 1122 1123 1125 1127 1133 1134 1135 1136 1137 1138
3 1 3 2 2 3 1 3 1 1 3 1 2
1140 1141 1142 1145 1146 1148 1151 1153 1155 1156 1158 1159 1160
2 2 1 1 1 2 2 1 3 1 3 1 3
1161 1162 1163 1164 1166 1168 1169 1170 1173 1174 1181 1182 1189
2 1 2 1 1 3 1 3 1 1 1 1 2
1191 1192 1193 1194 1201 1202 1203 1204 1210 1212 1213 1214 1221
3 3 2 3 1 1 1 1 4 4 2 3 3
1223 1224 1225 1226 1227 1228 1229 1230 1231 1236 1238 1240 1242
3 3 3 3 3 2 3 1 2 3 2 3 3
1243 1244 1246 1247 1249 1250 1251 1252 1253 1254 1256 1257 1259
3 3 3 3 3 2 3 2 2 3 3 1 3
1261 1262 1263 1264 1267 1268 1270 1271 1272 1273 1274 1275 1276
1 3 2 2 1 3 3 3 1 3 3 3 1
1277 1280 1281 1282 1283 1285 1287 1288 1289 1290 1291 1292 1293
3 3 1 1 3 2 3 1 2 2 3 2 3
1295 1297 1298 1301 1304 1307 1313 1316 1317 1319 1320 1324 1325
2 3 1 3 2 3 1 3 3 3 3 2 1
1326 1330 1334 1340 1342 1344 1347 1349 1350 1353 1354 1356 1358
3 1 1 3 1 1 2 2 1 1 2 1 1
1359 1360 1361 1362 1364 1365 1369 1370 1373 1374 1376 1378 1383
2 1 1 2 1 1 1 1 1 1 2 1 1
1385 1386 1387 1388 1393 1394 1397 1400 1403 1406 1408 1409 1412
3 1 1 2 2 1 2 1 1 3 1 2 3
1417 1418 1420 1421 1422 1424 1425 1429 1430 1431 1433 1434 1436
1 1 2 1 2 1 1 1 1 3 3 2 1
1437 1439 1440 1443 1444 1445 1447 1449 1451 1453 1460 1461 1463
3 3 4 2 2 2 1 2 3 3 1 1 1
1464 1466 1467 1468 1470 1471 1472 1473 1474 1476 1478 1479 1483
2 1 2 1 3 1 1 2 1 2 2 1 1
1486 1490 1491 1492 1495 1496 1497 1498 1500 1503 1504 1505 1507
1 2 1 1 1 3 2 2 1 1 1 3 1
1509 1510 1511 1514 1516 1517 1518 1519 1526 1528 1531 1533 1537
1 1 3 2 1 3 1 2 1 1 2 1 2
1538 1539 1541 1545 1547 1548 1549 1554 1555 1556 1561 1564 1566
1 1 1 3 1 1 1 4 4 4 2 4 4
1567 1569 1570 1571 1572 1574 1575 1576 1577 1578 1579 1580 1582
4 4 3 4 4 2 2 2 2 2 2 2 4
1587 1588 1589 1590 1592 1593 1594 1595 1596 1598 1599 1603 1604
4 2 2 4 4 4 4 4 4 4 4 2 2
1606 1607 1609 1610 1611 1612 1614 1616 1618 1619 1623 1624 1625
2 2 2 2 2 2 2 4 3 4 2 3 2
1626 1629 1630 1632 1634 1636 1637 1638 1639 1641 1642 1643 1646
2 2 2 3 4 3 2 3 2 4 2 4 3
1647 1648 1649 1650 1656 1659 1660 1661 1663 1665 1667 1669 1670
2 4 4 4 3 3 2 2 2 4 2 2 4
1671 1672 1673 1676 1677 1679 1681 1682 1683 1689 1690 1692 1693
4 4 3 4 4 2 4 4 4 2 4 2 2
1694 1696 1698 1699 1700 1707 1708 1709 1712 1714 1718 1719 1722
2 4 2 4 3 3 2 4 4 2 2 4 4
1723 1724 1729 1732 1733 1734 1735 1736 1737 1740 1741 1742 1744
4 4 2 4 4 4 4 4 4 4 4 2 2
1747 1748 1749 1752 1753 1754 1755 1756 1758 1759 1760 1763 1767
2 2 2 4 4 4 4 2 3 4 2 2 3
1768 1770 1771 1772 1775 1777 1778 1781 1782 1783 1785 1786 1795
4 3 3 3 2 4 2 4 4 2 2 3 2
1797 1798 1799 1800 1801 1802 1805 1806 1807 1809 1810 1812 1813
3 4 2 2 2 2 2 4 2 2 2 4 3
1814 1816 1817 1821 1823 1824 1825 1827 1828 1830 1831 1833 1835
4 2 3 4 2 2 2 3 4 2 2 2 2
1837 1838 1841 1842 1846 1847 1848 1849 1850 1851 1853 1854 1855
3 4 2 2 4 4 4 4 3 2 2 4 2
1858 1860 1862 1868 1869 1871 1874 1875 1876 1878 1879 1881 1882
4 4 4 2 4 2 2 4 3 4 3 2 2
1883 1884 1889 1890 1892 1893 1895 1896 1899 1901 1903 1904 1905
3 4 4 3 4 4 2 2 4 4 2 4 3
1906 1907 1910 1913 1914 1917 1919 1921 1926 1928 1929 1931 1933
4 2 4 2 4 3 4 2 3 4 2 4 3
1934 1935 1936 1939 1941 1942 1943 1944 1945 1947 1948 1950 1955
3 2 3 2 3 2 2 3 3 4 4 4 2
1959 1960 1961 1962 1964 1966 1967 1971 1972 1973 1974 1975 1976
2 2 4 4 4 3 4 3 2 4 4 3 4
1978 1979 1981 1982 1984 1986 1988 1989 1990 1994 1996 1997 1999
4 4 2 3 2 2 3 2 4 4 3 2 4
2000 2001 2002 2003 2006 2007 2009 2011 2015 2019 2020 2021 2022
2 4 2 4 4 2 4 3 2 3 2 4 2
2023 2026 2027 2029 2031 2032 2034 2035 2036 2038 2041 2042 2043
3 4 2 3 2 4 4 2 4 3 2 2 2
2045 2046 2047 2048 2050 2051 2053 2054 2055 2057 2059 2060 2061
4 2 4 4 2 3 3 2 2 2 2 4 2
2062 2063 2065 2066 2068 2069 2070 2071 2072 2073 2074 2076 2077
4 3 2 3 4 2 4 2 4 3 4 4 4
2080 2083 2084 2085 2086 2088 2089 2090 2092 2094 2095 2096 2098
4 4 2 3 2 4 2 3 2 4 4 2 3
2100 2105 2110 2112 2115 2117 2119 2121 2123 2124 2126 2129 2132
2 4 4 2 2 2 2 3 2 4 2 4 3
2133 2135 2136 2137 2138 2140 2141 2142 2145 2147 2148 2149 2150
2 4 2 4 4 3 4 2 4 4 4 4 4
2151 2152 2153 2154 2155 2156 2157 2161 2163 2166 2168 2173 2177
4 4 2 3 3 2 2 2 2 4 3 3 4
2181 2182 2184 2185 2188 2189 2190 2191 2192 2195 2196 2198 2201
3 4 2 2 4 2 3 3 4 3 4 4 4
2202 2209 2210 2211 2212 2215 2216 2220 2225 2227 2229 2230 2231
2 2 2 4 2 3 3 2 4 2 3 2 2
2232 2235 2237 2241 2243 2244 2245 2246 2247 2249 2250 2252 2254
2 4 4 4 3 3 4 2 4 4 4 3 2
2255 2256 2258 2259 2260 2261 2265 2269 2271 2274 2275 2276 2279
2 4 2 2 4 3 2 3 4 3 4 3 3
2280 2281 2282 2284 2285 2288 2289 2290 2297 2299 2301 2303 2304
4 2 3 1 3 2 2 2 3 2 2 4 2
2305 2307 2309 2310 2311 2312 2314 2316 2317 2318 2320 2321 2322
4 4 2 2 3 4 2 2 2 2 3 2 4
2323 2328 2331 2333 2334 2335 2337 2341 2342 2344 2345 2346 2347
2 2 3 3 4 4 3 3 3 2 3 2 3
2348 2349 2351 2352 2354 2355 2356 2357 2358 2359 2363 2364 2365
4 2 4 4 2 2 4 3 4 4 3 3 3
2366 2368 2369 2370 2373 2375 2376 2377 2378 2382 2383 2388 2389
4 4 4 3 2 3 3 3 2 3 3 3 4
2390 2393 2394 2395 2397 2398 2401 2403 2404 2407 2408 2409 2411
3 4 2 4 2 2 2 2 3 3 4 3 3
2413 2414 2415 2417 2419 2421 2422 2424 2425 2426 2427 2429 2430
2 3 3 4 3 3 3 3 2 2 3 2 3
2431 2432 2433 2440 2441 2442 2443 2446 2447 2450 2451 2454 2456
3 2 3 3 4 2 3 3 2 2 3 2 3
2457 2458 2460 2464 2465 2467 2470 2473 2474 2476 2479 2480 2481
1 1 1 1 1 3 3 3 1 2 3 2 2
2482 2484 2490 2493 2494 2495 2502 2505 2506 2509 2510 2511 2513
2 1 1 3 2 1 1 1 2 3 2 2 1
2514 2516 2518 2519 2520 2524 2532 2536 2537 2540 2541 2542 2543
2 2 2 1 2 3 3 2 3 2 2 3 2
2544 2546 2547 2548 2553 2554 2558 2559 2560 2561 2562 2566 2567
3 2 2 1 2 1 2 1 3 1 3 3 2
2568 2569 2571 2572 2575 2576 2577 2578 2580 2582 2587 2588 2589
2 2 2 2 2 2 1 2 2 3 1 1 2
2590 2591 2592 2596 2598 2599 2600 2603 2607 2608 2611 2614 2618
2 1 2 1 3 2 3 3 1 3 1 2 2
2619 2620 2623 2625 2626 2627 2628 2629 2631 2633 2636 2638 2639
3 3 1 3 1 2 3 2 2 1 2 1 2
2640 2642 2643 2646 2649 2650 2651 2652 2653 2654 2655 2656 2662
3 2 3 2 1 2 2 2 3 2 2 2 2
2668 2670 2671 2672 2673 2675 2677 2680 2682 2683 2686 2688 2691
3 2 2 2 2 3 2 3 3 2 3 2 3
2692 2693 2694 2696 2697 2698 2699 2700 2704 2705 2708 2709 2710
2 3 3 3 1 2 2 2 1 3 2 2 4
2711 2712 2713 2715 2716 2717 2720 2722 2723 2725 2726 2727 2737
3 2 4 1 2 2 3 2 3 2 3 1 3
2740 2741 2742 2747 2748 2749 2750 2753 2757 2758 2760 2763 2764
2 2 2 2 2 2 1 3 2 2 2 1 2
2765 2767 2768 2769 2771 2777 2779 2786 2788 2789 2790 2791 2792
1 1 3 3 3 1 2 2 1 2 2 4 4
2794 2795 2796 2797 2799 2800 2801 2803 2807 2808 2810 2812 2817
2 4 3 3 3 3 4 3 3 3 1 3 3
2818 2820 2822 2824 2825 2826 2827 2829 2830 2831 2835 2836 2837
3 1 2 3 3 2 3 1 3 1 1 1 1
2839 2841 2842 2843 2844 2846 2847 2848 2849 2852 2854 2855 2856
2 2 3 3 1 2 2 2 2 2 1 2 1
2858 2860 2863 2865 2866 2867 2874 2877 2878 2880 2882 2887 2888
1 2 3 1 2 1 3 2 1 3 3 2 1
2890 2892 2894 2895 2898 2900 2901 2905 2907 2909 2910 2911 2915
1 1 3 2 1 2 1 1 3 2 2 3 2
2917 2920 2922 2924 2925 2926 2930 2932 2935 2936 2942 2943 2944
1 1 1 1 3 2 2 1 2 1 1 1 1
2946 2947 2950 2952 2953 2954 2957 2958 2960 2962 2964 2965 2969
1 2 1 1 1 1 1 1 1 1 3 1 1
2975 2979 2980 2981 2983 2985 2986 2988 2990 2994 2996 2997 2999
4 2 3 3 3 3 2 3 3 3 2 2 3
3003 3006 3008 3010 3012 3013 3018 3023 3024 3025 3026 3027 3028
2 2 3 2 3 2 3 2 3 2 3 2 2
3030 3033 3034 3036 3038 3039 3043 3044 3045 3047 3049 3051 3052
2 3 3 3 3 3 3 3 2 3 3 3 3
3053 3055 3056 3058 3059 3062 3064 3065 3068 3069 3070 3071 3072
2 3 2 3 3 3 2 3 3 2 2 3 3
3073 3075 3077 3078 3083 3086 3093 3095 3097 3098 3099 3101 3103
2 2 3 2 2 2 2 2 3 3 2 2 2
3110 3111 3112 3113 3114 3115 3116 3118 3119 3121 3122 3123 3125
2 2 2 2 2 3 2 2 2 3 3 2 2
3127 3128 3130 3132 3133 3134 3136 3137 3138 3139 3140 3142 3143
3 2 2 2 2 2 3 3 2 3 2 3 2
3145 3146 3150 3152 3155 3159 3161 3167 3169 3178 3179 3181 3182
2 3 3 3 3 2 3 2 2 3 3 2 3
3184 3185 3187 3189 3190 3193 3194 3196 3197 3198 3200 3201 3202
2 3 3 2 3 3 2 2 2 2 2 2 2
3204 3205 3209 3211 3212 3214 3216 3217 3220 3222 3223 3224 3226
3 3 2 3 2 3 3 3 2 3 3 3 2
3229 3230 3231 3233 3236 3237 3239 3247 3248 3249 3250 3252 3255
3 2 2 2 3 3 2 3 3 2 2 3 3
3256 3260 3262 3265 3266 3267 3269 3271 3272 3273 3274 3276 3282
2 3 2 3 3 2 2 2 3 3 3 3 3
3285 3287 3291 3294 3297 3298 3299 3300 3301 3302 3303 3305 3309
2 2 2 2 3 3 2 2 2 3 2 2 3
3311 3315 3317 3318 3321 3322 3323 3326 3327 3328 3329 3331 3334
2 3 3 3 3 3 2 3 3 2 2 3 3
3337 3338 3341 3343 3347 3348 3350 3355 3357 3358 3364 3370 3372
2 3 3 3 2 3 3 3 2 3 3 3 2
3373 3374 3376 3380 3381 3382 3383 3389 3391 3394 3395 3396 3403
3 3 2 2 2 2 2 2 2 2 3 3 2
3407 3408 3409 3411 3413 3414 3415 3418 3419 3420 3422 3423 3427
3 3 3 3 3 3 2 2 2 2 3 2 2
3431 3432 3433 3434 3438 3441 3444 3448 3450 3452 3453 3455 3456
2 3 2 2 2 2 2 2 2 3 2 2 2
3457 3458 3459 3462 3464 3465 3467 3469 3470 3473 3474 3476 3477
2 3 2 3 3 2 2 3 3 2 2 2 2
3479 3480 3481 3482 3483 3484 3487 3490 3494 3495 3497 3498 3499
3 3 3 2 2 2 3 2 2 2 2 2 2
3500 3502 3503 3504 3505 3510 3511 3512 3515 3516 3517 3518 3521
2 3 2 3 2 2 3 2 3 2 2 2 3
3523 3524 3525 3528 3529 3531 3532 3537 3538 3540 3543 3545 3546
3 2 3 2 2 3 3 2 3 2 2 2 2
3548 3549 3551 3552 3555 3556 3557 3558 3560 3563 3564 3566 3567
3 3 3 3 2 3 3 2 3 2 3 2 2
3568 3570 3571 3573 3575 3576 3577 3578 3579 3580 3584 3585 3587
2 3 3 3 2 2 2 3 2 3 3 2 2
3588 3589 3590 3591 3601 3604 3605 3607 3608 3609 3610 3611 3612
3 3 2 3 2 2 3 2 2 3 3 2 3
3614 3616 3617 3618 3624 3625 3627 3628 3631 3634 3635 3637 3640
3 2 3 2 2 2 2 2 3 2 3 3 2
3641 3643 3644 3645 3646 3647 3648 3653 3654 3655 3658 3661 3663
2 3 2 3 2 3 2 2 3 3 3 2 3
3664 3666 3668 3669 3671 3672 3673 3675 3677 3679 3680 3684 3685
2 2 3 2 3 3 2 2 2 3 2 3 3
3686 3692 3693 3695 3696 3698 3699 3700 3701 3702 3703 3707 3709
2 4 3 3 2 3 3 3 3 3 3 3 2
3710 3712 3713 3714 3715 3716 3718 3719 3720 3721 3726 3731 3735
2 2 2 3 3 2 2 2 3 3 2 3 3
3737 3739 3741 3742 3745 3746 3748 3752 3753 3754 3755 3756 3757
3 3 3 2 2 2 2 3 2 2 3 3 3
3760 3761 3762 3764 3767 3771 3772 3781 3784 3787 3788 3790 3791
2 2 3 3 3 2 3 2 3 2 3 2 2
3793 3795 3796 3797 3801 3802 3804 3805 3807 3810 3811 3815 3817
2 3 2 1 3 3 3 3 3 2 2 3 3
3818 3822 3823 3825 3826 3830 3831 3832 3834 3835 3836 3838 3839
3 3 2 3 3 3 3 3 3 3 3 3 4
3840 3845 3847 3848 3849 3850 3851 3852 3853 3854 3857 3858 3859
3 3 3 3 3 3 3 3 3 3 3 3 3
3860 3861 3863 3867 3868 3873 3875 3876 3877 3881 3882 3883 3885
3 1 2 3 3 3 3 3 1 3 3 2 3
3886 3887 3888 3890 3891 3892 3893 3896 3898 3901 3904 3905 3907
3 1 2 2 4 3 3 3 3 3 3 3 3
3908 3913 3915 3916 3921 3923 3924 3926 3928 3931 3932 3934 3935
1 2 2 2 4 3 3 3 2 3 2 3 3
3941 3942 3943 3944 3952 3953 3954 3957 3958 3959 3963 3964 3965
1 3 2 3 3 3 3 3 3 3 3 3 3
3967 3970 3973 3974 3975 3977 3978 3979 3982 3983 3985 3986 3987
3 4 3 3 3 3 3 3 3 3 1 3 3
3988 3989 3994 3996 3998 3999 4000 4001 4004 4005 4007 4010 4013
3 3 3 1 3 3 1 3 2 3 3 3 3
4016 4017 4019 4020 4021 4022 4023 4024 4025 4026 4027 4028 4029
1 2 2 3 3 3 3 3 3 3 3 3 1
4031 4034 4037 4039 4041 4043 4044 4047 4049 4050 4051 4053 4058
3 3 3 3 2 3 4 1 3 1 3 3 3
4063 4064 4066 4071 4073 4074 4076 4077 4078 4080 4082 4086 4087
3 3 3 3 3 3 1 3 4 3 3 4 4
4088 4089 4090 4091 4092 4093 4094 4095 4098 4099 4104 4105 4106
3 3 2 3 3 3 3 3 3 4 3 3 2
4107 4108 4109 4111 4112 4114 4116 4119 4120 4121 4124 4125 4128
3 3 3 1 3 1 3 2 3 3 3 3 3
4129 4131 4132 4134 4137 4139 4141 4144 4145 4146 4147 4148 4151
3 3 3 3 2 3 3 3 3 3 3 3 3
4152 4154 4155 4156 4157 4162 4166 4167 4169 4170 4171 4172 4173
3 3 3 3 3 3 4 3 3 3 2 3 3
4175 4177 4178 4180 4181 4184 4185 4186 4190 4194 4197 4198 4200
4 2 2 3 1 3 3 4 1 3 3 4 2
4204 4205 4207 4210 4212 4213 4216 4217 4220 4221 4223 4226 4230
4 3 2 4 1 1 3 3 3 4 1 1 3
4231 4240 4241 4242 4244 4245 4249 4250 4251 4254 4255 4256 4257
2 4 1 1 3 4 3 4 3 1 1 3 3
4259 4260 4261 4269 4271 4272 4274 4276 4277 4278 4280 4282 4283
1 1 1 4 4 1 1 1 3 1 4 1 1
4287 4288 4289 4290 4291 4293 4294 4295 4297 4298 4301 4303 4305
3 3 2 3 4 1 1 2 1 1 1 4 3
4306 4308 4309 4310 4313 4314 4315 4316 4317 4318 4320 4321 4323
1 3 1 3 1 4 1 4 2 4 3 1 1
4325 4326 4328 4329 4330 4332 4333 4336 4337 4339 4341 4342 4343
3 4 2 1 3 4 3 3 3 1 4 1 1
4344 4347 4352 4353 4356 4357 4358 4359 4361 4362 4363 4367 4368
3 3 3 1 3 4 1 3 1 4 1 1 4
4369 4370 4371 4372 4375 4376 4378 4379 4380 4382 4383 4384 4386
4 2 4 4 3 4 3 1 1 3 1 1 3
4387 4388 4390 4391 4392 4394 4395 4400 4401 4404 4405 4406 4409
3 3 1 3 1 1 3 1 1 1 1 1 1
4411 4413 4415 4416 4422 4423 4427 4430 4431 4432 4434 4435 4438
1 3 2 2 3 4 3 2 2 2 1 1 2
4439 4441 4444 4446 4447 4449 4450 4451 4452 4454 4455 4456 4459
1 2 4 3 3 2 2 2 1 1 2 2 2
4460 4461 4463 4468 4474 4475 4476 4479 4481 4484 4485 4488 4489
2 1 2 4 2 2 3 3 4 2 1 2 4
4491 4493 4494 4499 4500 4501 4502 4503 4504 4506 4507 4509 4510
3 3 3 4 2 1 4 3 3 1 3 3 3
4511 4514 4516 4517 4518 4528 4531 4532 4533 4537 4538 4540 4542
4 1 2 3 2 4 2 3 2 3 4 2 2
4543 4544 4545 4546 4547 4550 4551 4554 4559 4561 4562 4563 4566
3 2 3 3 2 2 3 4 2 1 3 3 3
4568 4569 4570 4572 4573 4574 4577 4578 4579 4580 4584 4589 4590
1 2 2 3 2 3 1 4 4 4 4 3 2
4592 4594 4595 4596 4597 4598 4599 4600 4601 4602 4605 4606 4608
2 3 2 1 2 2 1 4 2 3 3 2 2
4609 4612 4616 4617 4619 4620 4621 4622 4623 4624 4625 4628 4629
3 1 2 3 2 2 3 3 2 1 4 2 2
4631 4632 4639 4641 4644 4646 4647 4649 4652 4654 4656 4657 4659
1 3 2 4 1 3 3 3 2 4 2 1 2
4660 4664 4666 4667 4670 4671 4673 4674 4675 4676 4677 4678 4680
2 3 3 2 4 2 4 3 3 1 2 4 2
4681 4683 4684 4685 4687 4688 4689 4690 4691 4692 4693 4694 4695
3 2 2 4 1 2 2 2 3 4 3 2 2
4697 4698 4699 4701 4702 4704 4707 4708 4712 4714 4715 4716 4717
2 3 2 1 3 2 2 2 2 1 3 1 3
4722 4724 4729 4730 4734 4735 4737 4739 4740 4742 4743 4745 4746
2 2 3 1 3 2 2 2 3 1 4 2 3
4749 4750 4753 4754 4757 4760 4764 4768 4769 4771 4772 4773 4775
3 2 4 2 4 4 1 4 4 3 1 2 1
4776 4777 4779 4780 4781 4782 4784 4785 4790 4793 4796 4797 4800
3 2 3 3 1 2 3 4 4 2 3 2 2
4802 4804 4806 4807 4813 4814 4816 4817 4819 4820 4821 4824 4826
3 1 4 3 1 4 4 2 4 2 3 3 4
4830 4831 4833 4834 4837 4838 4839 4841 4843 4845 4847 4849 4850
3 3 3 1 4 2 2 2 1 3 3 4 3
4851 4852 4854 4856 4857 4858 4862 4864 4865 4866 4868 4869 4873
3 2 1 2 3 3 2 2 1 3 3 3 2
4874 4878 4879 4882 4886 4887 4889 4892 4894 4896 4897 4899 4900
2 3 2 4 3 3 4 4 3 3 3 2 3
4901 4904 4905 4906 4908 4909 4910 4911 4912 4913 4914 4916 4917
3 3 1 3 2 3 1 2 2 2 2 4 2
4918 4919 4920 4921 4924 4925 4926 4927 4928 4929 4931 4932 4936
2 3 2 1 1 1 4 3 3 3 3 3 4
4939 4940 4943 4944 4950 4951 4952 4953 4955 4957 4959 4961 4962
2 2 3 1 2 1 2 2 2 1 3 4 1
4964 4965 4966 4967 4968 4971 4972 4974 4976 4977 4979 4981 4982
2 4 4 2 2 2 2 2 1 2 3 3 4
4983 4984 4986 4988 4991 4992 4995 4997 4998 4999 5001 5007 5009
4 2 3 2 3 3 3 4 3 2 3 3 2
5010 5011 5015 5016 5017 5018 5021 5024 5025 5026 5027 5028 5030
3 3 3 2 3 2 2 2 2 2 2 2 2
5031 5032 5036 5039 5041 5044 5045 5046 5047 5048 5051 5052 5053
4 3 4 4 4 2 4 2 3 2 2 3 2
5054 5057 5061 5062 5063 5064 5066 5067 5068 5070 5074 5077 5078
4 4 2 3 3 2 2 2 3 2 1 2 3
5079 5081 5083 5084 5085 5086 5087 5088 5090 5091 5094 5096 5097
2 2 3 3 1 1 1 4 1 2 2 4 1
5099 5101 5102 5106 5109 5111 5113 5116 5119 5120 5124 5125 5126
2 3 2 3 4 4 2 4 2 2 4 2 1
5127 5128 5129 5130 5132 5133 5135 5137 5138 5139 5142 5143 5144
1 3 2 2 3 2 3 2 4 1 3 4 2
5145 5148 5150 5152 5153 5154 5155 5156 5157 5159 5164 5165 5166
2 2 1 3 3 4 1 3 1 1 1 4 3
5167 5170 5171 5172 5174 5175 5179 5181 5185 5187 5188 5189 5190
3 4 2 2 3 1 2 1 2 3 4 1 1
5192 5193 5194 5201 5202 5204 5205 5207 5209 5210 5213 5214 5216
3 1 1 3 3 4 3 4 4 1 1 4 3
5218 5223 5224 5227 5233 5235 5236 5239 5240 5243 5245 5247 5248
3 4 1 3 4 2 3 2 2 4 2 3 2
5250 5251 5253 5254 5259 5262 5264 5265 5266 5273 5275 5277 5278
4 4 4 3 2 3 1 4 1 1 2 2 3
5284 5286 5287 5288 5290 5292 5293 5294 5295 5298 5300 5301 5302
2 1 3 2 2 2 1 3 1 2 2 1 1
5303 5305 5306 5307 5311 5313 5315 5316 5320 5321 5322 5325 5326
2 4 2 2 2 3 3 2 2 3 2 2 3
5327 5328 5329 5331 5334 5335 5336 5340 5343 5344 5345 5346 5348
2 2 2 2 4 3 4 4 3 4 4 3 2
5350 5351 5352 5353 5354 5355 5356 5358 5359 5360 5363 5366 5367
3 1 3 3 2 3 2 2 2 3 2 4 2
5368 5369 5371 5373 5375 5376 5377 5379 5381 5382 5383 5387 5389
2 2 2 3 1 4 2 3 2 4 2 2 4
5390 5391 5393 5394 5395 5396 5397 5398 5399 5400 5401 5404 5405
3 2 2 3 1 2 3 2 3 3 3 3 3
5407 5408 5409 5411 5412 5413 5417 5418 5420 5421 5423 5425 5427
1 2 1 3 2 3 1 2 2 2 2 3 4
5428 5429 5430 5431 5433 5434 5437 5438 5439 5441 5442 5443 5444
2 2 2 1 3 2 2 3 2 2 4 2 3
5446 5451 5453 5454 5455 5457 5458 5459 5460 5461 5464 5466 5467
4 4 3 4 2 3 2 3 4 2 2 3 2
5468 5469 5472 5473 5474 5475 5481 5485 5486 5487 5488 5489 5491
4 2 2 4 3 2 4 3 3 4 4 3 2
5493 5494 5495 5498 5499 5506 5509 5510 5511 5514 5516 5517 5518
4 4 4 4 2 4 4 4 2 2 2 4 2
5519 5521 5522 5525 5527 5533 5536 5537 5538 5540 5543 5544 5548
3 4 2 2 4 4 4 2 4 3 3 1 3
5549 5551 5554 5556 5558 5559 5563 5566 5567 5568 5569 5571 5574
4 3 2 2 2 3 2 2 1 2 2 1 3
5575 5576 5577 5578 5580 5581 5583 5584 5587 5589 5590 5591 5592
2 4 1 1 1 1 3 3 1 3 3 1 1
5593 5596 5597 5598 5601 5603 5604 5605 5608 5609 5610 5611 5612
1 3 1 1 1 1 1 1 2 1 1 1 1
5613 5617 5619 5621 5623 5624 5627 5628 5630 5631 5635 5638 5641
4 4 3 4 4 4 4 4 3 4 3 2 2
5643 5645 5646 5647 5652 5655 5656 5657 5658 5660 5662 5663 5664
3 4 4 4 3 4 4 4 4 4 3 3 3
5665 5668 5670 5671 5674 5675 5678 5679 5681 5683 5684 5687 5688
3 3 3 3 3 3 3 3 3 3 3 2 2
5690 5691 5692 5693 5694 5695 5696 5697 5698 5699 5703 5705 5710
2 3 3 3 2 3 3 3 3 3 3 3 1
5711 5715 5716 5717 5718 5720 5721 5723 5726 5727 5728 5729 5730
2 3 2 3 3 3 3 3 3 2 3 3 4
5732 5733 5735 5737 5738 5740 5741 5743 5745 5746 5750 5751 5753
2 3 1 3 3 3 1 4 4 1 3 4 4
5756 5757 5760 5761 5766 5769 5772 5774 5779 5780 5781 5783 5784
4 3 1 3 1 1 3 1 1 4 3 1 3
5788 5789 5790 5792 5793 5795 5796 5799 5803 5807 5809 5812 5813
4 1 1 4 3 3 1 1 1 1 1 1 1
5814 5815 5816 5817 5818 5822 5824 5827 5829 5830 5831 5832 5833
1 1 4 1 4 4 4 1 1 1 1 4 1
5835 5838 5840 5842 5843 5844 5845 5846 5847 5850 5851 5856 5857
1 4 1 3 2 4 1 3 1 1 1 4 3
5858 5859 5863 5864 5866 5869 5870 5871 5872 5874 5875 5878 5879
1 1 1 4 1 1 3 1 4 1 1 1 4
5880 5884 5885 5886 5887 5889 5890 5891 5892 5893 5894 5895 5900
4 4 1 1 4 1 4 1 3 4 4 4 4
5904 5906 5909 5910 5911 5913 5914 5921 5923 5926 5928 5930 5931
1 1 4 1 1 1 1 4 1 1 1 4 4
5932 5934 5936 5937 5938 5940 5943 5944 5946 5949 5950 5951 5952
1 1 4 1 4 1 4 1 1 2 1 1 1
5954 5957 5958 5959 5960 5961 5964 5965 5969 5972 5975 5976 5978
4 1 4 1 1 1 1 4 2 4 1 1 1
5980 5986 5987 5989 5990 5992 5993 5994 5995 5997 6005 6006 6009
2 4 1 4 1 1 1 1 4 4 1 4 1
6010 6013 6014 6015 6017 6019 6020 6021 6022 6025 6027 6029 6030
4 1 4 1 4 4 1 3 3 2 1 1 4
6031 6035 6036 6037 6038 6041 6042 6043 6046 6047 6053 6055 6056
1 1 1 3 1 4 3 1 3 4 1 4 1
6060 6063 6064 6065 6066 6067 6068 6069 6070 6071 6072 6076 6077
1 1 4 4 4 1 4 1 1 1 1 4 1
6078 6079 6082 6084 6085 6086 6087 6088 6090 6093 6098 6099 6100
1 1 1 1 4 1 2 4 1 4 1 1 4
6101 6104 6106 6107 6108 6109 6110 6111 6115 6116 6118 6120 6122
4 3 4 1 1 1 4 3 1 4 1 1 4
6126 6127 6128 6129 6131 6136 6137 6139 6140 6141 6143 6145 6147
1 1 3 1 1 1 3 1 3 1 1 4 1
6148 6149 6150 6151 6152 6154 6155 6158 6160 6163 6165 6166 6167
4 4 1 1 1 4 1 1 3 4 1 4 1
6169 6170 6172 6177 6179 6180 6181 6183 6186 6188 6191 6195 6200
4 1 1 4 1 4 4 1 4 1 4 1 1
6205 6206 6208 6210 6211 6212 6213 6214 6216 6217 6218 6220 6223
3 1 1 1 1 4 1 4 4 1 4 1 1
6224 6225 6226 6229 6230 6231 6232 6234 6235 6236 6237 6238 6239
1 4 1 4 2 2 4 1 4 1 1 4 1
6240 6241 6242 6243 6244 6245 6246 6248 6251 6252 6255 6256 6257
4 4 1 3 1 3 1 1 1 1 4 1 1
6259 6260 6261 6263 6264 6265 6266 6267 6269 6271 6272 6274 6277
4 4 3 4 1 1 4 4 1 3 1 1 1
6278 6279 6280 6282 6283 6284 6287 6290 6292 6293 6294 6295 6296
4 2 4 4 1 4 4 4 4 1 4 1 1
6297 6298 6300 6301 6303 6304 6308 6313 6315 6320 6321 6323 6328
1 1 1 1 1 1 4 1 1 1 1 1 3
6331 6332 6334 6335 6336 6337 6338 6340 6342 6344 6345 6348 6349
1 1 1 4 1 4 1 1 4 1 1 4 4
6351 6352 6354 6357 6358 6359 6362 6364 6365 6366 6369 6372 6373
4 1 1 1 4 4 4 4 1 1 4 2 3
6375 6376 6377 6380 6381 6383 6384 6385 6386 6387 6390 6391 6392
1 4 4 4 4 4 1 1 1 1 4 4 1
6393 6394 6395 6396 6397 6400 6403 6404 6405 6407 6409 6410 6413
4 4 4 1 1 1 1 1 1 4 4 1 1
6414 6416 6417 6419 6422 6425 6428 6429 6430 6431 6432 6433 6434
1 4 1 1 1 4 3 4 1 4 4 4 1
6435 6437 6439 6440 6441 6443 6444 6446 6447 6450 6451 6452 6453
1 4 1 1 4 1 1 4 4 4 1 1 4
6455 6457 6458 6459 6460 6461 6463 6466 6469 6471 6472 6473 6476
4 4 1 1 1 1 4 4 1 4 4 1 1
6477 6479 6480 6481 6482 6483 6484 6486 6487 6488 6491 6493 6494
1 4 1 1 4 3 4 1 4 1 1 4 1
6495 6496 6497 6498 6503 6506 6507 6513 6514 6516 6518 6521 6525
1 1 1 1 1 3 1 1 4 1 1 1 4
6526 6527 6529 6530 6532 6533 6534 6535 6536 6540 6544 6548 6549
3 3 1 1 4 1 1 1 4 1 4 4 1
6550 6551 6553 6555 6556 6557 6562 6563 6564 6565 6567 6569 6573
4 1 1 4 2 4 1 1 1 4 1 4 1
6577 6584 6586 6587 6589 6591 6592 6593 6594 6595 6598 6602 6603
4 1 3 4 1 3 4 1 4 3 1 4 3
6605 6606 6609 6610 6612 6614 6615 6616 6618 6619 6623 6624 6625
1 4 4 1 4 4 4 1 4 1 1 1 4
6626 6627 6630 6632 6634 6635 6638 6640 6641 6642 6644 6645 6647
1 4 1 1 4 4 1 2 1 1 1 1 1
6651 6655 6656 6657 6659 6661 6664 6665 6666 6669 6670 6671 6673
4 3 3 3 2 3 3 1 2 2 2 3 1
6677 6681 6684 6686 6687 6688 6691 6692 6694 6696 6697 6698 6699
3 3 3 1 1 3 1 1 1 2 1 1 1
6700 6702 6706 6707 6708 6710 6711 6712 6713 6714 6715 6716 6717
2 2 1 1 1 1 2 1 1 1 1 1 1
6719 6720 6721 6725 6727 6728 6729 6730 6731 6732 6735 6736 6740
1 2 3 2 3 2 1 1 2 1 3 2 1
6743 6744 6745 6749 6753 6755 6757 6758 6760 6762 6763 6765 6767
1 2 3 1 3 2 1 1 2 2 1 1 1
6769 6770 6771 6772 6773 6780 6781 6783 6785 6786 6787 6789 6790
1 1 3 2 1 3 3 4 3 2 3 1 1
6791 6794 6795 6796 6797 6798 6799 6800 6804 6805 6806 6810 6812
3 3 1 1 3 1 1 2 3 2 1 1 2
6813 6816 6823 6824 6825 6828 6829 6830 6831 6836 6839 6841 6842
4 1 1 1 1 1 1 2 1 1 1 2 1
6845 6847 6852 6853 6857 6858 6860 6861 6862 6866 6867 6868 6869
1 1 1 1 1 1 1 1 2 3 1 1 2
6871 6872 6874 6878 6879 6881 6882 6884 6886 6887 6888 6889 6891
1 3 1 2 1 1 1 1 2 1 2 2 1
6892 6893 6895 6899 6903 6905 6906 6908 6910 6911 6913 6914 6916
1 3 1 1 2 4 4 4 4 4 2 2 4
6920 6922 6923 6927 6928 6929 6930 6931 6935 6936 6938 6939 6941
2 2 2 4 2 4 4 4 4 2 2 4 4
6943 6944 6945 6946 6948 6950 6953 6954 6955 6959 6960 6961 6962
4 2 2 4 2 2 4 3 2 2 4 2 4
6963 6965 6968 6969 6970 6972 6973 6974 6978 6979 6980 6983 6985
2 4 2 4 1 2 1 2 3 2 1 4 2
6986 6987 6991 6992 6997 6998 6999 7003 7004 7005 7006 7007 7009
2 4 3 4 2 4 3 4 4 2 4 4 4
7011 7014 7015 7016 7017 7018 7019 7021 7022 7023 7027 7030 7032
4 4 2 3 2 3 3 2 4 2 3 2 2
7034 7036 7039 7040 7041 7042 7043 7044 7045 7048 7049 7052 7053
1 2 2 2 1 2 2 1 2 1 2 3 3
7054 7055 7059 7061 7062 7064 7065 7067 7070 7071 7072 7075 7076
2 1 2 1 2 1 1 1 2 2 1 1 1
7077 7078 7079 7081 7082 7085 7086 7088 7089 7094 7099 7101 7102
1 1 2 2 1 1 1 1 2 1 2 1 2
7105 7106 7108 7109 7110 7111 7113 7115 7117 7119 7120 7121 7122
1 1 3 2 2 2 3 1 1 1 3 1 1
7124 7125 7126 7128 7129 7131 7135 7137 7138 7142 7143 7144 7147
1 1 1 1 1 1 1 1 2 2 2 1 2
7151 7152 7153 7158 7163 7169 7170 7175 7178 7179 7180 7181 7183
2 2 1 1 1 3 1 2 2 3 2 1 2
7185 7186 7187 7188 7189 7191 7193 7195 7196 7199 7201 7202 7203
1 3 2 1 1 2 2 3 1 1 1 2 1
7208 7209 7211 7212 7215 7217 7219 7222 7226 7227 7229 7230 7233
3 3 1 2 2 1 3 2 2 3 1 2 2
7234 7236 7238 7241 7245 7246 7247 7249 7250 7253 7258 7259 7261
1 1 1 2 2 1 1 1 1 1 1 1 1
7262 7264 7266 7270 7272 7273 7274 7275 7277 7279 7281 7282 7286
1 2 3 1 1 1 1 1 1 1 1 1 1
7287 7296 7300 7304 7306 7307 7310 7311 7312 7315 7316 7317 7319
1 1 2 1 2 1 1 1 1 1 1 1 1
7321 7322 7326 7329 7330 7331 7335 7336 7337 7338 7339 7344 7347
1 1 2 4 4 3 3 2 4 2 4 3 4
7350 7351 7354 7356 7357 7358 7360 7361 7363 7366 7367 7368 7369
4 2 3 4 4 4 2 4 4 4 4 4 4
7370 7371 7373 7374 7375 7376 7377 7383 7385 7386 7387 7389 7390
4 3 4 3 3 4 4 3 4 2 4 4 2
7393 7395 7399 7400 7402 7403 7404 7406 7407 7409 7411 7412 7415
4 4 3 3 4 3 4 3 4 4 3 4 4
7416 7417 7421 7422 7423 7424 7425 7426 7427 7428 7429 7430 7431
4 3 3 4 2 2 3 4 3 4 4 3 2
7432 7434 7438 7442 7443 7446 7448 7449 7450 7453 7454 7455 7459
3 4 3 4 3 3 3 3 2 2 4 4 4
7461 7462 7464 7466 7473 7475 7477 7479 7480 7483 7484 7485 7489
4 3 4 4 2 4 4 4 4 2 3 4 3
7490 7493 7494 7495 7498 7500 7501 7503 7504 7506 7510 7513 7515
4 4 4 4 3 2 3 3 2 3 3 3 3
7518 7519 7522 7523 7524 7527 7528 7530 7531 7532 7534 7536 7544
3 3 3 3 2 3 2 3 2 3 2 3 3
7545 7546 7548 7549 7550 7551 7552 7553 7554 7555 7556 7557 7558
3 2 4 3 3 3 3 2 3 2 3 2 3
7559 7560 7563 7564 7566 7569 7570 7573 7579 7582 7583 7584 7586
3 3 2 3 2 2 2 2 2 3 3 3 2
7588 7589 7591 7595 7601 7606 7608 7609 7610 7613 7615 7620 7621
3 2 3 3 3 3 3 2 3 2 3 3 3
7622 7626 7627 7630 7632 7634 7635 7636 7637 7639 7641 7642 7644
2 2 3 2 3 3 3 3 2 2 3 3 3
7647 7649 7652 7653 7654 7656 7658 7662 7666 7667 7668 7670 7671
3 3 3 3 2 2 2 3 3 2 3 3 3
7672 7673 7675 7678 7683 7684 7685 7686 7688 7690 7691 7694 7700
3 3 2 3 3 3 3 1 4 2 3 3 3
7703 7706 7707 7709 7710 7712 7713 7715 7716 7717 7720 7721 7722
2 2 1 1 1 3 3 3 1 1 4 3 1
7723 7725 7728 7729 7731 7732 7733 7735 7736 7737 7738 7740 7741
3 3 3 3 1 3 1 4 1 3 4 3 1
7746 7747 7750 7751 7752 7757 7760 7761 7762 7766 7772 7773 7775
2 3 3 2 2 3 3 3 3 4 1 1 3
7777 7778 7779 7783 7784 7785 7787 7788 7789 7791 7792 7793 7795
3 1 3 3 3 2 1 4 1 1 3 3 4
7796 7799 7800 7805 7809 7812 7815 7816 7819 7821 7822 7823 7825
1 4 4 1 3 1 4 3 1 3 1 3 4
7829 7838 7839 7840 7841 7842 7843 7844 7846 7847 7848 7849 7850
3 1 3 4 3 3 1 1 3 3 1 1 3
7851 7853 7854 7855 7856 7857 7858 7859 7862 7865 7868 7869 7871
3 1 3 3 3 3 1 3 1 2 4 4 1
7874 7875 7876 7877 7878 7879 7880 7882 7884 7885 7887 7890 7891
1 1 1 1 4 1 1 3 2 2 4 4 4
7892 7893 7898 7899 7900 7901 7902 7904 7907 7908 7909 7910 7912
1 4 3 1 4 4 1 3 4 3 1 1 1
7913 7915 7916 7919 7923 7924 7925 7926 7927 7929 7930 7931 7932
3 1 2 3 1 1 2 4 1 4 3 3 3
7936 7937 7938 7939 7940 7943 7945 7947 7948 7949 7950 7953 7954
1 1 4 1 4 3 2 1 3 3 1 3 3
7955 7957 7963 7964 7966 7967 7969 7973 7974 7975 7976 7981 7985
3 4 1 3 3 1 1 4 3 1 3 3 3
7989 7992 7994 7996 7997 7998 8000 8001 8002 8003 8004 8006 8008
4 1 2 4 1 1 1 1 1 1 1 1 1
8012 8015 8017 8018 8022 8023 8025 8026 8028 8029 8033 8034 8037
1 1 4 1 1 4 3 1 1 4 1 1 4
8038 8042 8044 8045 8048 8049 8052 8054 8055 8056 8057 8058 8059
1 1 1 1 4 1 1 1 1 1 1 4 1
8061 8062 8063 8066 8069 8071 8072 8076 8077 8078 8080 8081 8082
4 1 4 1 3 4 1 4 1 1 4 1 1
8083 8084 8085 8088 8090 8093 8094 8098 8099 8100 8102 8104 8107
4 4 1 1 4 2 4 1 1 3 1 4 1
8109 8111 8112 8113 8114 8116 8117 8118 8123 8124 8126 8127 8129
4 4 4 4 4 1 1 1 1 1 4 4 1
8130 8131 8132 8135 8141 8142 8143 8144 8147 8148 8149 8151 8152
4 1 1 1 1 4 4 1 1 1 4 1 1
8153 8155 8156 8159 8160 8161 8162 8163 8165 8169 8170 8171 8176
1 1 1 1 1 1 4 1 1 4 1 4 4
8177 8179 8181 8183 8184 8186 8187 8189 8190 8194 8196 8197 8199
4 2 1 3 1 1 4 2 1 3 3 1 1
8201 8206 8208 8209 8210 8212 8213 8215 8217 8218 8220 8221 8222
1 1 4 1 4 4 4 1 4 3 1 1 1
8223 8225 8227 8230 8233 8234 8235 8236 8238 8239 8240 8243 8244
1 4 1 4 4 4 1 1 1 1 1 1 1
8245 8246 8248 8249 8251 8252 8253 8256 8258 8259 8260 8263 8264
1 3 1 3 4 4 4 1 4 4 1 1 1
8268 8269 8270 8271 8274 8275 8277 8279 8280 8281 8283 8284 8285
1 3 4 1 1 1 3 1 4 1 1 1 3
8286 8287 8289 8290 8291 8293 8294 8296 8299 8301 8302 8303 8305
1 4 1 4 1 4 4 1 4 3 1 1 4
8306 8308 8309 8312 8314 8315 8318 8319 8320 8321 8322 8323 8329
4 1 4 1 1 4 1 1 1 4 1 1 1
8330 8332 8333 8334 8335 8337 8338 8339 8340 8343 8344 8346 8347
4 1 1 1 4 1 1 1 1 1 1 4 1
8353 8354 8356 8358 8364 8366 8370 8371 8372 8373 8374 8375 8376
4 1 4 1 4 1 3 2 3 1 1 4 3
8377 8378 8379 8380 8381 8382 8384 8385 8389 8390 8394 8395 8396
4 1 1 4 1 4 1 1 1 4 4 3 1
8399 8400 8401 8402 8403 8404 8406 8412 8413 8415 8416 8419 8420
1 4 4 1 3 4 3 3 1 1 1 4 4
8421 8423 8427 8428 8429 8431 8432 8435 8437 8438 8439 8440 8442
4 4 2 3 1 1 4 1 1 1 3 1 1
8445 8446 8447 8449 8451 8452 8458 8459 8461 8462 8463 8465 8469
1 1 1 1 4 1 4 1 1 1 3 1 4
8471 8472 8473 8474 8476 8479 8480 8481 8482 8484 8485 8486 8488
4 1 4 4 4 3 1 1 4 4 1 4 1
8491 8493 8494 8495 8498 8500 8502 8504 8505 8506 8507 8508 8509
3 1 4 1 3 3 4 3 3 1 4 1 4
8511 8512 8514 8515 8516 8517 8519 8527 8528 8529 8530 8533 8535
4 1 1 4 4 2 3 3 1 4 1 4 1
8536 8541 8543 8544 8547 8548 8550 8551 8552 8555 8556 8562 8563
1 4 4 1 1 1 4 1 4 3 3 4 1
8565 8567 8568 8569 8571 8573 8574 8575 8577 8578 8580 8581 8582
1 1 1 1 4 1 1 1 1 1 4 1 1
8583 8584 8585 8587 8591 8593 8596 8598 8600 8602 8603 8604 8605
4 4 1 4 1 1 1 4 1 4 4 4 1
8610 8611 8613 8615 8616 8617 8618 8619 8620 8621 8622 8623 8627
1 1 1 1 4 1 1 1 4 1 1 1 1
8628 8629 8631 8632 8633 8635 8636 8639 8640 8641 8642 8647 8649
2 1 2 1 1 1 4 4 4 1 1 1 1
8651 8652 8654 8655 8658 8659 8660 8661 8662 8663 8665 8666 8667
1 1 4 1 1 1 1 1 1 1 3 1 3
8669 8671 8673 8674 8675 8677 8681 8683 8686 8690 8691 8693 8694
4 4 4 4 1 4 1 1 4 4 4 4 1
8695 8696 8699 8703 8704 8705 8706 8708 8709 8710 8711 8714 8716
1 4 1 4 1 1 1 1 1 4 1 2 1
8717 8718 8719 8724 8726 8728 8731 8735 8737 8744 8745 8747 8748
4 1 1 4 4 1 4 1 1 1 3 1 1
8749 8750 8751 8754 8756 8757 8759 8760 8762 8763 8764 8766 8767
1 4 4 4 2 3 1 1 1 1 4 1 1
8769 8770 8771 8773 8777 8783 8784 8785 8788 8790 8793 8794 8799
3 1 4 1 1 4 1 1 1 1 3 1 4
8801 8802 8803 8804 8807 8809 8810 8811 8814 8815 8817 8818 8820
3 3 2 4 4 4 3 1 4 1 3 3 1
8824 8826 8829 8831 8833 8834 8835 8836 8838 8839 8841 8842 8846
4 1 2 1 1 2 4 4 1 1 4 4 1
8847 8848 8850 8852 8853 8855 8858 8862 8863 8867 8868 8872 8873
4 4 1 4 4 4 1 1 4 4 3 1 4
8876 8880 8881 8884 8885 8888 8892 8893 8894 8895 8899 8901 8902
4 4 4 3 1 1 4 4 4 4 4 4 1
8903 8905 8906 8907 8909 8910 8912 8914 8915 8916 8920 8921 8926
4 4 4 3 4 1 1 1 1 1 4 1 3
8927 8930 8931 8932 8933 8934 8935 8937 8940 8941 8942 8945 8948
4 4 2 1 4 1 4 1 1 4 4 1 4
8954 8955 8957 8958 8960 8963 8964 8966 8967 8968 8972 8973 8974
1 1 1 4 1 1 4 1 1 1 1 1 1
8977 8978 8981 8985 8989 8991 8992 8993 8994 8995 8996 8997 8999
3 1 4 1 2 1 1 1 4 1 1 4 4
9000 9001 9002 9003 9004 9005 9007 9008 9009 9012 9014 9016 9017
4 1 1 4 1 1 1 4 1 4 4 1 4
9018 9020 9021 9022 9024 9027 9028 9030 9031 9032 9035 9036 9037
1 1 1 1 4 1 1 1 2 3 1 3 4
9038 9039 9042 9043 9046 9048 9049 9050 9051 9054 9055 9058 9059
3 1 3 4 1 4 1 4 4 3 1 1 3
9061 9063 9070 9071 9074 9076 9079 9082 9083 9084 9085 9087 9088
1 3 3 1 4 4 1 4 4 1 4 3 1
9091 9092 9094 9096 9098 9102 9103 9105 9107 9108 9109 9113 9114
1 4 4 4 1 1 4 3 1 1 4 3 3
9116 9117 9118 9120 9124 9125 9126 9128 9130 9132 9134 9136 9137
4 4 3 1 1 4 1 4 4 1 1 3 3
9138 9141 9142 9146 9147 9155 9156 9158 9160 9162 9168 9171 9172
1 1 1 1 4 4 4 1 1 1 4 4 1
9173 9174 9175 9176 9177 9178 9179 9180 9182 9184 9186 9187 9188
4 4 3 3 1 4 1 1 4 1 4 4 3
9194 9197 9198 9203 9204 9205 9206 9207 9208 9209 9210 9211 9212
4 4 1 2 1 1 1 4 1 1 1 1 4
9213 9214 9215 9218 9220 9221 9224 9225 9226 9227 9228 9229 9231
1 4 4 4 4 4 2 3 2 4 1 2 2
9232 9235 9239 9241 9242 9244 9245 9247 9248 9249 9250 9252 9253
1 4 3 1 4 1 4 1 1 4 1 1 1
9255 9256 9261 9262 9263 9265 9267 9269 9270 9272 9274 9275 9276
1 4 1 4 4 1 1 3 1 3 4 1 1
9277 9280 9281 9285 9289 9295 9296 9297 9300 9303 9305 9306 9307
1 1 4 4 2 3 1 1 1 1 1 4 1
9308 9309 9313 9314 9315 9316 9317 9318 9319 9320 9322 9326 9328
1 1 1 1 1 1 4 4 1 4 1 4 1
9329 9331 9332 9333 9334 9335 9336 9338 9339 9340 9341 9342 9343
4 1 4 3 1 1 3 4 3 4 1 4 4
9344 9345 9346 9348 9351 9352 9353 9356 9357 9358 9359 9360 9362
4 1 1 1 4 1 1 3 1 4 4 4 1
9364 9365 9367 9368 9369 9370 9372 9373 9374 9375 9378 9379 9381
1 1 3 1 1 1 2 4 1 4 1 1 3
9382 9384 9385 9386 9387 9388 9389 9391 9392 9393 9394 9395 9396
4 4 4 4 1 1 4 4 4 4 1 1 3
9397 9398 9399 9402 9403 9406 9407 9411 9413 9415 9416 9417 9418
1 1 4 4 1 1 1 4 4 1 1 4 1
9419 9421 9422 9423 9424 9425 9426 9428 9429 9430 9431 9432 9436
4 1 1 1 1 4 1 4 1 1 4 1 3
9438 9439 9442 9444 9447 9452 9453 9454 9455 9457 9461 9463 9465
3 1 4 1 4 1 4 1 4 1 1 1 1
9467 9470 9471 9476 9480 9481 9482 9483 9484 9485 9487 9489 9490
4 1 1 1 1 4 4 1 1 4 4 1 1
9491 9495 9498 9499 9500 9502 9503 9505 9506 9507 9508 9509 9511
1 2 1 4 4 1 3 1 1 1 3 4 1
9512 9513 9514 9517 9518 9519 9522 9525 9526 9528 9530 9531 9532
2 4 1 3 4 4 4 4 1 1 1 4 4
9535 9537 9538 9539 9543 9545 9546 9547 9548 9551 9553 9554 9558
1 1 1 1 1 1 1 1 1 1 3 4 1
9561 9562 9563 9564 9568 9572 9573 9575 9576 9577 9578 9579 9582
4 4 4 1 4 4 1 4 4 1 1 1 4
9583 9585 9587 9588 9590 9592 9597 9598 9599 9600 9603 9604 9608
1 1 4 1 3 4 1 1 1 4 1 1 1
9609 9611 9612 9613 9616 9617 9618 9619 9620 9623 9624 9625 9627
1 4 2 1 4 4 1 4 4 4 1 1 1
9628 9631 9634 9635 9636 9638 9640 9641 9643 9648 9650 9652 9653
1 4 3 1 1 4 1 3 4 4 1 1 4
9654 9656 9658 9659 9661 9662 9663 9664 9666 9667 9669 9670 9673
1 1 1 1 1 1 1 4 4 4 4 3 4
9676 9682 9685 9686 9687 9688 9689 9690 9693 9696 9697 9699 9700
4 1 4 1 1 1 1 1 1 4 1 1 1
9701 9705 9708 9710 9711 9712 9713 9714 9715 9717 9718 9719 9720
1 1 1 1 1 1 1 4 1 1 4 1 1
9723 9724 9725 9726 9727 9728 9736 9737 9741 9743 9744 9746 9747
4 4 4 1 1 1 4 1 1 4 4 4 1
9748 9749 9751 9752 9754 9756 9757 9758 9759 9761 9762 9765 9766
1 4 1 4 1 4 4 1 1 1 1 1 1
9767 9768 9769 9771 9774 9776 9777 9778 9780 9783 9786 9787 9789
4 1 4 4 1 1 1 1 1 1 1 4 4
9790 9793 9795 9796 9797 9798 9799 9801 9802 9803 9804 9806 9807
4 4 1 1 4 1 4 1 1 4 1 4 1
9808 9810 9812 9813 9814 9815 9817 9819 9820 9823 9824 9826 9829
3 1 1 4 4 1 1 4 4 1 4 4 1
9832 9834 9835 9837 9840 9842 9843 9845 9846 9849 9850 9852 9855
1 1 1 1 4 4 1 3 1 1 4 4 4
9857 9860 9861 9863 9869 9870 9872 9873 9875 9876 9878 9879 9882
1 1 3 1 1 1 4 4 4 1 1 4 1
9883 9884 9885 9888 9890 9891 9892 9893 9894 9895 9896 9897 9898
4 3 3 1 1 3 4 4 1 1 1 1 1
9899 9902 9903 9904 9906 9907 9911 9912 9913 9914 9916 9917 9918
1 1 4 3 1 1 1 1 1 4 1 1 1
9919 9922 9923 9928 9931 9932 9933 9935 9939 9940 9941 9943 9945
1 1 4 3 1 1 1 1 4 1 3 1 4
9946 9948 9949 9950 9951 9953 9954 9957 9959 9960 9962 9966 9967
1 1 4 1 3 4 4 1 4 1 1 1 4
9968 9973 9974 9975 9977 9981 9982 9983 9984 9985 9988 9989 9990
1 1 1 4 4 1 4 3 3 4 1 4 4
9991 9992 9993 9996 9997 9998 10002 10004 10006 10007 10008 10009 10011
1 1 1 1 1 1 4 1 4 1 1 1 4
10013 10016 10017 10019 10020 10021 10025 10026 10028 10030 10031 10032 10033
1 4 4 1 1 4 1 1 1 4 4 4 1
10034 10036 10040 10041 10047 10048 10051 10052 10058 10059 10060 10061 10062
1 4 4 1 4 4 4 4 4 1 1 1 1
10063 10064 10065 10066 10069 10070 10071 10072 10074 10075 10078 10079 10081
1 2 4 3 4 4 1 1 4 1 3 1 4
10083 10091 10092 10093 10095 10096 10098 10105 10107 10108 10109 10110 10111
1 4 4 1 4 1 4 4 1 1 1 1 1
10113 10115 10117 10119 10120 10121 10122 10123 10124 10125 10126 10127 10129
1 4 1 4 4 1 1 1 1 1 4 1 1
10130 10133 10136 10139 10141 10144 10145 10146 10147 10149 10150 10151 10152
1 3 1 4 1 1 1 4 4 1 1 4 1
10154 10155 10156 10159 10162 10167 10168 10172 10177 10179 10182 10183 10184
1 1 4 1 3 4 3 1 1 1 1 4 1
10186 10187 10188 10189 10190 10192 10201 10202 10209 10211 10213 10220 10221
4 4 1 1 1 1 1 1 1 1 1 1 4
10222 10224 10225 10227 10228 10229 10230 10232 10236 10238 10242 10245 10246
4 1 1 4 4 1 4 4 4 3 1 1 1
10247 10250 10251 10252 10254 10258 10259 10260 10261 10263 10264 10265 10269
4 4 1 2 4 1 3 4 4 4 4 1 1
10270 10271 10274 10276 10278 10280 10282 10283 10286 10287 10288 10290 10291
1 4 1 1 4 1 1 1 1 1 1 4 3
10292 10295 10297 10302 10303 10304 10309 10313 10314 10316 10319 10321 10322
1 1 1 1 4 1 1 1 4 1 4 4 1
10323 10324 10325 10326 10331 10332 10333 10335 10336 10338 10339 10343 10346
1 1 3 1 4 4 1 1 4 1 1 1 4
10347 10351 10352 10353 10354 10357 10359 10362 10367 10368 10370 10372 10374
1 4 1 1 4 1 4 1 1 4 1 1 1
10375 10376 10378 10379 10380 10383 10385 10386 10388 10390 10392 10393 10394
1 1 4 1 4 1 1 3 1 4 1 1 4
10396 10397 10399 10400 10401 10406 10410 10413 10415 10418 10420 10421 10422
4 4 4 1 1 1 1 4 1 1 4 4 1
10427 10438 10440 10441 10445 10446 10447 10448 10452 10453 10454 10456 10457
1 4 4 1 4 4 4 1 1 4 1 1 1
10458 10459 10461 10462 10466 10467 10468 10469 10472 10474 10475 10476 10477
4 1 1 1 1 1 1 4 1 1 1 1 1
10478 10480 10481 10484 10486 10487 10491 10493 10495 10497 10498 10499 10500
1 4 1 4 1 1 4 1 1 1 1 1 1
10501 10504 10506 10507 10508 10510 10512 10514 10519 10523 10527 10528 10529
1 4 4 1 1 4 3 1 1 1 1 4 1
10531 10535 10537 10538 10539 10540 10541 10542 10543 10545 10549 10550 10551
1 1 4 4 4 1 1 1 4 4 4 4 1
10552 10555 10556 10558 10561 10563 10564 10565 10566 10568 10573 10576 10577
1 3 1 1 1 1 1 4 1 4 1 1 1
10578 10579 10581 10583 10586 10587 10588 10589 10591 10594 10595 10597 10598
3 4 4 1 4 1 1 1 1 1 1 4 1
10599 10600 10601 10604 10606 10608 10609 10610 10612 10614 10617 10618 10619
4 1 4 1 3 1 1 1 4 4 1 1 1
10622 10623 10626 10627 10629 10631 10632 10633 10635 10636 10637 10639 10641
4 4 1 1 1 4 1 1 1 3 1 1 4
10642 10643 10644 10645 10646 10648 10649 10650 10651 10653 10654 10655 10656
1 1 4 1 1 1 4 1 1 1 1 1 1
10657 10660 10664 10665 10666 10667 10668 10669 10670 10671 10672 10674 10676
1 4 1 4 1 4 4 4 1 1 3 4 4
10678 10680 10681 10683 10684 10687 10688 10695 10696 10697 10699 10700 10701
4 1 4 3 4 1 4 4 1 4 4 4 4
10702 10704 10706 10708 10714 10715 10716 10717 10719 10720 10722 10723 10725
4 1 1 4 3 1 4 4 3 3 4 4 1
10726 10727 10728 10729 10730 10733 10734 10736 10737 10739 10741 10746 10749
1 1 1 4 1 1 4 4 1 1 3 1 1
10754 10757 10758 10759 10760 10761 10764 10768 10771 10772 10776 10778 10780
1 1 4 1 4 1 1 1 1 1 4 1 4
10783 10786 10790 10791 10794 10795 10796 10800 10801 10802 10803 10806 10807
4 4 4 1 1 1 4 4 1 1 1 1 1
10815 10816 10817 10818 10819 10820 10823 10826 10827 10829 10830 10831 10832
1 4 1 4 4 4 4 4 4 1 4 1 1
10834 10835 10836 10839 10842 10849 10850 10852 10856 10858 10859 10863 10864
1 1 4 1 1 1 1 4 3 1 4 3 1
10865 10866 10867 10870 10872 10875 10876 10877 10879 10880 10883 10884 10888
1 4 4 3 1 3 1 4 4 1 4 4 4
10890 10891 10892 10893 10894
4 1 1 2 1
Within cluster sum of squares by cluster:
[1] 23246.70 23134.63 25881.56 21310.15
(between_SS / total_SS = 34.1 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault"
#plot k-mean
fviz_cluster(list(data = data2, cluster = km$cluster),
ellipse.type = "norm", geom = "point", stand = FALSE,
palette = "jco", ggtheme = theme_classic())
The given plot represents the result of a cluster analysis. In this analysis, data points are grouped together based on their similarities. The resulting plot visualizes these clusters on a two-dimensional plane.
In this case, there are four clusters represented by the colors yellow, green, blue, and red. Each color corresponds to a different cluster. The position of each point on the plane indicates the location of that point in the feature space, which is defined by the values of the features for that point. It is important to note that the position of each point within its cluster does not have any specific meaning or interpretation. It simply reflects the point's position in the feature space. Additionally, the number of data points within each cluster may not necessarily correspond to the number of points plotted for that cluster. The points in the cluster plot are simply the points from the dataset that were assigned to that cluster by the clustering algorithm.
#avg silhouette
library(cluster)
sil <- silhouette(km$cluster, dist(data))
rownames(sil) <- rownames(data)
fviz_silhouette(sil)
cluster size ave.sil.width 1 1 1813 0.66 2 2 1332 0.65 3 3 1389 -0.65 4 4 1360 0.60
#Total within-cluster-sum of square
km$tot.withinss
cluster_assignments <- c(km$cluster)
ground_truth_labels <- c(dataset$satisfaction)
data <- data.frame(cluster = cluster_assignments, label = ground_truth_labels)
# Function to calculate BCubed precision and recall
calculate_bcubed_metrics <- function(data) {
n <- nrow(data)
precision_sum <- 0
recall_sum <- 0
for (i in 1:n) {
cluster <- data$cluster[i]
label <- data$label[i]
# Count the number of items from the same category within the same cluster
same_category_same_cluster <- sum(data$label[data$cluster == cluster] == label)
# Count the total number of items in the same cluster
total_same_cluster <- sum(data$cluster == cluster)
# Count the total number of items with the same category
total_same_category <- sum(data$label == label)
# Calculate precision and recall for the current item and add them to the sums
precision_sum <- precision_sum + same_category_same_cluster /total_same_cluster
recall_sum <- recall_sum + same_category_same_cluster / total_same_category
}
# Calculate average precision and recall
precision <- precision_sum / n
recall <- recall_sum / n
return(list(precision = precision, recall = recall))
}
# Calculate BCubed precision and recall
metrics <- calculate_bcubed_metrics(data)
# Extract precision and recall from the metrics
precision <- metrics$precision
recall <- metrics$recall
# Print the results
cat("BCubed Precision:", precision, "\n")
cat("BCubed Recall:", recall, "\n")
BCubed Precision: 0.6205272 BCubed Recall: 0.3168307
6.2.4 - Analysis:¶
| k=2 | k=3 | k=4 | |
|---|---|---|---|
| Average Silhouette width | 0.19 | 0.27 | 0.33 |
| total within-cluster sum of square | 114475.5 | 101393.9 | 93573.03 |
| BCubed precision | 0.624 | 0.628 | 0.620 |
| BCubed recall | 0.63 | 0.42 | 0.316 |
| Visualization | all of the figures is shown above | all of the figures is shown above | all of the figures is shown above |
- Upon analyzing the provided table for k=2,k=3,k=4 :
A reduced silhouette width for K=2 implies less distinct separation between clusters. However, the BCubed precision and recall exhibit a relatively balanced performance. When determining the number of clusters, it is essential to weigh the trade-off between interpretability and clustering performance.
The Total Within-Cluster Sum of Squares (WSS) is a k-means clustering metric that gauges the cumulative squared distances between data points and the centroids of their assigned clusters. Analyzing WSS across varying values of k (2, 3, 4) sheds light on the compactness of the resulting clusters.
BCubed precision assesses clustering accuracy concerning ground truth, while recall evaluates completeness. Notably, for K=3, precision is higher but recall is lower compared to K=2 and K=4. This indicates a trade-off scenario between precision and recall, emphasizing the need to carefully balance these metrics based on the specific goals and requirements of the clustering task.
7- Findings :¶
- Classification¶
In these stages, our team meticulously assembled a dataset containing valuable information about clients, with the aim of predicting the satisfaction of future customers with the airline's services. Our overarching objective was to provide the airline company with essential insights and preemptive measures to enhance their service quality. To ensure the precision and reliability of our results, we applied diverse preprocessing techniques to refine the dataset, thereby improving its efficiency for subsequent analysis. Additionally, we employed various plotting methods to visually explore the dataset, gaining a profound understanding of its characteristics and identifying optimal preprocessing steps. Drawing insights from our visualizations and utilizing relevant commands, we systematically addressed issues such as missing or outlier values. Instances posing challenges were systematically removed from the dataset to safeguard the accuracy of our predictions. Furthermore, data transformation, involving the normalization and discretization of specific attributes, was performed to standardize attribute weights and simplify data handling in subsequent data mining tasks.
Our collective efforts aimed to establish an efficient and dependable predictive model. After the preprocessing stage, we applied diverse methods, including the Gini index, gain ratio, and information gain, employing various partitioning techniques. The outcomes of each method were carefully evaluated to ascertain the most suitable approach tailored to the specifics of our dataset. Regarding information gain, after analyzing three different splits (training 70% and test 30%, training 60% and test 40%, training 85% and test 15%), the accuracies were close in all three scenarios. Thus, we believe the 85% training and 15% test split strikes a good balance and exhibits the highest sensitivity (0.772), indicating a superior ability to capture relevant positive cases. Additionally, it boasts the highest specificity (0.930), signifying a superior ability to correctly identify negative cases. For the Gini index, using an identical division, the 70% training and 30% test split stood out for its top accuracy and specificity, demonstrating a harmonious balance between training and testing. Conversely, the 85% training and 15% test split surpassed others in precision, indicating heightened accuracy in positive predictions. Despite fluctuations in precision, sensitivity, and specificity across different splits, no discernible pattern emerged to suggest a consistently superior split. The accuracies remained closely aligned in all three scenarios, highlighting a consistent and reliable overall model performance. However, considering the balance in detail, we conclude that the 70% training and 30% test split is the most favorable among them. In gain ratio, after analyzing three different splits (training 70% and test 30%, training 60% and test 40%, training 85% and test 15%), it becomes evident that the 60% training and 40% test split exhibits slightly superior performance in terms of accuracy, precision, sensitivity, and specificity compared to the other splits. The metrics consistently favor the 60% training and 40% test split, suggesting a better overall model performance in this configuration. However, for a more conclusive decision, additional steps such as cross-validation and considerations specific to the domain context should be undertaken. It's important to note that without insights into feature splits and gain ratio details, a comprehensive analysis within that specific framework remains challenging. Therefore, further investigation is recommended for a more informed and nuanced understanding of the model's performance across different training and test splits.
We believe that the decision tree utilizing the gain index is optimal for our classification task, given its efficiency and interpretability. However, we maintain a willingness to explore alternative impurity measures to fine-tune our model's performance further. Moreover, upon evaluating different training and test splits, it becomes evident that the model employing the Gini index, particularly with a "training (85%) and test (15%)" split, consistently exhibits robust performance. This configuration demonstrates high accuracy, precision, sensitivity, and specificity, showcasing a balanced capability in capturing positive cases, identifying negative cases, and providing an overall accurate model. Given the metrics presented and in the absence of additional context, the "training (85%) and test (15%)" split utilizing the Gini index emerges as a favorable choice for our decision tree model.
- Clustering¶
all of the figures are shown above¶
In our comprehensive exploration of the optimal number of clusters (K) using the K-means algorithm, we systematically scrutinized three distinct scenarios (K=2, K=3, K=4) by employing multiple clustering metrics. The average silhouette width, a pivotal measure delineating cluster cohesion and separation, consistently demonstrated an upward trend with an increasing number of clusters, underscoring enhanced cluster definition. Simultaneously, the total within-cluster sum of squares (WSS), a reflection of cluster compactness, manifested a discernible decrease across scenarios, particularly accentuated with higher values of K.
The k=2 partition displayed moderate performance metrics, indicating clusters that were not distinctly separated. The average silhouette width of 0.19 signaled a fair but not ideal level of separation between clusters, hinting at some overlap among them. The high total within-cluster sum of squares, reaching 114475.5, highlighted that the data points within clusters were widely spread from their respective centroids, indicating substantial dispersion. Despite relatively decent BCubed precision and recall scores of 0.624 and 0.63, respectively, signifying a moderate level of agreement within clusters and a good ability to capture relevant instances, the clusters appeared to intermingle, lacking clear boundaries or differentiation from one another. This overlapping nature could potentially complicate the interpretation and utility of these clusters for distinct categorization or analysis
for k=3 unveils noticeable enhancements compared to the previous partition (k=2). The average silhouette width of 0.27 signifies improved cluster separation, indicating that the data points are more distinctly grouped into three clusters with better-defined boundaries. This suggests an advancement in clustering quality, although some overlap between clusters might still persist. The decrease in the total within-cluster sum of squares to 101393.9 demonstrates tighter clustering, with data points more closely located around their respective cluster centroids compared to the k=2 configuration. Additionally, the BCubed precision score of 0.628 showcases a relatively strong agreement within clusters. However, the decline in BCubed recall to 0.42 implies a challenge in capturing all relevant instances within these more refined clusters. Despite the better-defined clusters and enhanced precision, the trade-off involves a reduction in recall, highlighting the difficulty in comprehensively encompassing all true positives within these distinct clusters in the k=3 partition. Overall, k=3 exhibits improved cluster definition and tighter grouping but at the expense of a diminished ability to capture all relevant instances comprehensively.
for k=4 demonstrates continued refinement in cluster separation and cohesion compared to the previous partitions. With an average silhouette width of 0.33, this configuration signifies improved distinction between clusters, indicating a clearer grouping of data points into four clusters with more defined boundaries than in the k=3 scenario. The reduced total within-cluster sum of squares, now at 93573.03, reflects even tighter clustering, with data points more closely centered around their respective cluster centroids, portraying more compact and cohesive clusters. The BCubed precision, standing at 0.620, maintains a reasonable level of agreement within these clusters, akin to the earlier partitions. However, the BCubed recall further diminishes to 0.316, emphasizing the challenge in capturing all pertinent instances within these highly refined clusters. Despite achieving better-defined clusters and sustained precision, the trade-off includes a notable decline in recall, underscoring the difficulty in comprehensively identifying all true positives within the clusters as the number of clusters increases. In essence, k=4 delivers a more distinct and tightly clustered arrangement, yet it struggles to encompass all relevant instances comprehensively within these refined clusters, highlighting the ongoing trade-off between precision and recall as clusters become more granular.
Clustring OR Classification ?¶
After a comprehensive analysis, it appears that the clustering outcomes may not be entirely suitable for our dataset. Despite attempts with varying cluster counts, the results consistently showcase challenges. The clusters generated, particularly in higher counts like k=3 and k=4, exhibit limitations in effectively capturing the inherent patterns or distinctions within our dataset. The diminishing recall alongside the moderate precision suggests that the clusters, despite their refinement, fail to comprehensively represent the diverse range of instances present in our data. Hence, it's apparent that the clustering approaches applied might not adequately align with the underlying structure or characteristics of our dataset. So classification is more suitable for predicting the satisfaction.
Solution¶
The application of the decision tree with the Gini index revealed valuable insights and provided effective solutions to enhance the predictive modeling of customer satisfaction for an airline company. The challenge of determining the optimal training-test split was systematically addressed through thorough evaluation, ultimately identifying the "training (85%) and test (15%)" split as the most favorable. This split consistently demonstrated robust performance, showcasing high accuracy, precision, sensitivity, and specificity. The decision tree model, utilizing the Gini index with this split, emerged as the optimal choice for predicting customer satisfaction. Furthermore, the willingness to explore alternative impurity measures indicated a commitment to continuous model refinement, ensuring adaptability to potential nuances in the dataset.
On the other hand, the application of K-means clustering illuminated challenges and potential solutions for cluster analysis. The exploration of different cluster counts (K=2, K=3, K=4) provided insights into trade-offs between cluster separation and cohesion. While K=2 exhibited moderate performance, K=3 showed improved definition with a trade-off between precision and recall, and K=4 delivered more distinct clusters but struggled with comprehensive instance capture. This information suggested that traditional K-means clustering might not be entirely suitable for the dataset, especially with higher cluster counts. The recommendation is to carefully consider the trade-offs observed, possibly exploring alternative clustering methods or incorporating additional features to better align with the dataset's characteristics.
In conclusion, both the decision tree with the Gini index and K-means clustering techniques contributed valuable insights and actionable solutions. The decision tree provided an optimized predictive model for customer satisfaction, emphasizing the importance of tailored training-test splits and continuous model refinement. Meanwhile, the K-means clustering analysis shed light on the challenges associated with traditional clustering approaches, prompting a reconsideration of methods and an exploration of alternative solutions to better capture the dataset's underlying patterns.
10-References :¶
[1] A. Jha, "In-Depth Intuition of K-Means Clustering Algorithm in Machine Learning," Analytics Vidhya, Jan. 2021. [Online]. Available: https://www.analyticsvidhya.com/blog/2021/01/in-depth-intuition-of-k-means-clustering-algorithm-in-machine-learning/
[2] D. Alboukadel, "K-Means Clustering in R: Algorithm and Practical Examples," DataNovia, [Online]. Available: https://www.datanovia.com/en/lessons/k-means-clustering-in-r-algorith-and-practical-examples/
[3] Stack Overflow, "Package Cannot be Unloaded in R - Cannot Install Package," [Online]. Available: https://stackoverflow.com/questions/32771517/package-cannot-be-unloaded-in-r-cannot-install-package
[4] R Documentation, "as.matrix.confusionMatrix," Caret Package, [Online]. Available: https://rdrr.io/cran/caret/man/as.matrix.confusionMatrix.html
[5] F. Juraev, "Airlines Customer Satisfaction Classification," Kaggle, [Online]. Available: https://www.kaggle.com/code/firuzjuraev/airlines-customer-satisfaction-classification
[6] A. Thevapalan and J. Le, "R Decision Trees Tutorial: Examples & Code in R for Regression & Classification," www.datacamp.com, Jan. 27, 2023. https://www.datacamp.com/tutorial/decision-trees-R